GRADUATE PROGRAM HANDBOOK€¦ · Academic Regulations 8 All applications are processed and assessed by the graduate program committee (GPC) and approved by the graduate studies committee

Academic Regulations 1

GRADUATE PROGRAM

HANDBOOK

SEPTEMBER 2018


Contents

1. Mission of the University of Science and Technology ................................................................................................ 7

2. Graduate General Academic Policies & Regulations .................................................................................................. 7

2.1. Introduction ............................................................................................................................................................. 7

2.2. Meng, M.Sc. And Ph.D. Graduate Disciplines .............................................................................................. 8

2.3. Transitional Arrangements ................................................................ Error! Bookmark not defined.

2.4. MEng Degree ........................................................................................................................................................... 8

2.4.1. Admission Criteria For MEng Degrees ..................................................................................................... 8

2.4.2. Duration And Requirements For Granting MEng Degrees .............................................................. 9

2.4.3. Coursework Requirements For MEng Graduate Programs ............................................................ 9

2.4.4. MEng Capstone Project ................................................................................................................................ 10

2.5. M.Sc. Degree ........................................................................................................................................................... 10

2.5.1. Admission Criteria For M.Sc. Degrees .................................................................................................... 10

2.5.2. Duration And Requirements For M.Sc. Degrees ................................................................................ 11

2.5.3. Coursework Requirements For M.Sc. Graduate Programs ............................................................ 12

2.5.4. M.Sc. Thesis ....................................................................................................................................................... 12

2.6. Regulations For Ph.D. Degrees ....................................................................................................................... 13

2.6.1. Ph.D. Admission Criteria .............................................................................................................................. 13

2.6.2. Duration of Ph.D. Program .......................................................................................................................... 14

2.6.3. Ph.D. Supervision Committee .................................................................................................................... 14

2.6.4. Ph.D. Requirements ....................................................................................................................................... 14

2.6.5. Ph.D. Dissertation ........................................................................................................................................... 15

2.6.6. Data Defence ..................................................................................................................................................... 15

2.6.7. Ph.D. Final Examination Committee and Thesis Defense............................................................... 16

2.6.8. Graduate Academic Advisor ....................................................................................................................... 16

2.6.9. Scholarly Work during Graduate Program .......................................................................................... 16

3. Academic Regulations ...................................................................................................................................................... 17

3.1. Registration ........................................................................................................................................................... 17

3.2. Graduate Grading System ................................................................................................................................ 17

3.3. Grade Point Average........................................................................................................................................... 18

3.4. Incomplete Work ................................................................................................................................................. 18

3.5. Obtained Grade ..................................................................................................................................................... 18

3.6. Change of Grades ................................................................................................................................................. 18

3.7. Repeating Course ................................................................................................................................................. 19

3.8. Transfer of Credits .............................................................................................................................................. 19

3.9. Withdrawal ............................................................................................................................................................ 19

3.9.1. Withdrawing from Courses ........................................................................................................................ 19


3.9.2. Withdrawal from the University .............................................................................................................. 19

3.10. Dismissal ................................................................................................................................................................. 20

4. Program Thesis ................................................................................................................................................................... 21

4.1. Dissertation / Thesis Format ......................................................................................................................... 21

4.2. Preparation for Submission of Thesis ......................................................................................................... 21

4.3. Preparation for the Oral Defence .................................................................................................................. 21

5. Student Academic Record & Transcripts ................................................................................................................. 22

5.1. Student’s Academic Record ............................................................................................................................. 22

6. Code of Conduct at University of Science & Technology ................................................................................... 23

6.1. Impetus .................................................................................................................................................................... 23

6.2. Student Rights within the University Community ................................................................................. 23

6.3. Academic Integrity Policy ................................................................................................................................ 24

a. Plagiarism .................................................................................................................................................................... 24

b. Fabrication .................................................................................................................................................................. 24

c. Cheating........................................................................................................................................................................ 24

d. Other Academic Misconduct ................................................................................................................................ 25

7. Curricula and Study Plans 2017/2018 ..................................................................................................................... 26

7.1. M.Sc. in Biomedical sciences (36 Cr) ........................................................................................................... 26

7.1.1. Program Description ..................................................................................................................................... 26

7.1.2. Program Educational Objectives (PEOs) .............................................................................................. 26

7.1.3. Students Outcomes ........................................................................................................................................ 26

7.1.4. Courses Categories ......................................................................................................................................... 27

7.1.5. Degree Requirements for M.Sc. in biomedical sciences .................................................................. 27

7.1.6. University Requirements ............................................................................................................................ 28

7.1.7. Program Requirements ................................................................................................................................ 28

7.1.8. concentration Requirements ..................................................................................................................... 29

7.1.9. Study Plan for M.Sc. in Biomedical sciences ........................................................................................ 31

7.2. Ph.D. in biomedical sciences (60 Cr) ........................................................................................................... 32

7.2.1. Study Plan for Ph.D. in BIOMEDICAL SCIENCES ................................................................................ 32

7.2.2. Study Plan for Ph.D. in Biomedical sciences ........................................................................................ 32

7.3. M.Sc. in Nano- Science (36 Cr) ....................................................................................................................... 34





7.3.5. Degree Requirements for M.Sc. in Nano- Science.............................................................................. 35



7.3.8. Study Plan for M.Sc. in Nano- Science .................................................................................................... 37


7.4. Ph.D. in Nano- Science (60 Cr) ....................................................................................................................... 38

7.4.1. Coursework Requirements ......................................................................................................................... 38

7.4.2. Study Plan for Ph.D. in Nano-Science ..................................................................................................... 38

7.5. M.Sc. in Physics (36 Cr) ..................................................................................................................................... 40





7.5.5. Degree Requirements for M.Sc. in Physics ........................................................................................... 41



7.5.8. Study Plan for M.Sc. in Physics of Earth and Universe .................................................................... 42

7.6. Ph.D. in Physics (60 Cr) ..................................................................................................................................... 43


7.6.2. Study Plan for Ph.D. in Physics .................................................................................................................. 43

7.7. M.Sc. in Mathematics (36 Cr) .......................................................................................................................... 45





7.7.5. Degree Requirements for M.Sc. in Mathematics ................................................................................ 46



7.8. Ph.D. in Mathematics (60 Cr) .......................................................................................................................... 49


7.8.2. Study Plan for Ph.D. in Mathematics ....................................................................................................... 49

7.9. M.Sc. in Cyber Security (Joint between CIE and Math Programs) (36 Cr) ................................... 51





7.9.5. Degree Requirements for M.Sc. in Cyber Security ............................................................................ 52



7.9.8. Study Plan for M.Sc. in Cyber Security ................................................................................................... 54

7.10. M.Sc. in Artificial Intelligence (Joint between CIE and Math Programs) (36 Cr) ...................... 54






7.10.5. Degree Requirements for M.Sc. in Artificial Intelligence ............................................................... 56



7.10.8. Study Plan for M.Sc. in Artificial Intelligence ...................................................................................... 58

7.11. M.Sc. in Nanotechnology and Nanoelectronics Engineering (36 Cr) ............................................. 59







7.11.7. Study Plan for M.Sc. in Nanotechnology Engineering ...................................................................... 62

7.12. Ph.D. in Nanotechnology and Nanoelectronics Engineering (60 Cr) ............................................. 64

7.12.1. Study Plan for Ph.D. in Nanotechnology Engineering ..................................................................... 64

7.13. M.Sc. in Environmental Engineering (36 Cr) ........................................................................................... 66







7.13.7. Study Plan for M.Sc. in Environmental Engineering ........................................................................ 69

7.14. Ph.D. in Environmental Engineering (60 Cr) ........................................................................................... 70


7.14.2. Study Plan for Ph.D. in Environmental Engineering ........................................................................ 70

7.15. MEng in Environmental Engineering (33 Cr) .......................................................................................... 72





7.15.5. Compulsory and Elective Courses ........................................................................................................... 73

7.15.6. Study Plan for MEng in Environmental Engineering ....................................................................... 74

8. Course Catalog 2018/2019 ............................................................................................................................................ 76

8.1. Common Courses Catalog ............................................................................................................................ 76

8.2. Graduate Programs Course Catalog ...................................................................................................... 76

8.2.1. Course Catalog for GRADUATE BMS Program ........................................................................... 76

8.2.2. Course Catalog for Graduate Nano-Science Program ............................................................ 83

8.2.3. Course Catalog for Graduate Physics Program ......................................................................... 87


8.2.4. Course Catalog for Graduate Math Program ............................................................................... 93

8.2.5. Course Catalog for M.Sc. in Artificial Intelligence ............................................................... 103

8.2.6. Course Catalog for M.Sc. in Cyber Security .............................................................................. 106

8.2.7. Course Catalog for M.Sc. in Nanotechnology and Nanoelectronics Engineering 111

8.2.8. Course Catalog for PhD in Nanotechnology and Nanoelectronics Engineering . 121

8.2.9. Course Catalog for M.Sc. in Environmental Engineering ................................................. 121

8.2.10. Course Catalog for MEng in Environmental Engineering ........................................... 128

8.2.11. Course Catalog for PhD in Environmental Engineering .............................................. 133


1. MISSION OF THE UNIVERSITY OF SCIENCE AND TECHNOLOGY The mission of the University of Science and Technology (UST) is to serve the people of Egypt through pre-eminence in creating, communicating, and applying knowledge, through science, research, technology, and academic values. UST strives to develop leaders and citizens who challenge the present and enrich the future. UST prepares students to embark on 21st century knowledge society guided by our policy: “Admission to the university is only based on merit”. The University is defined by a culture of interdisciplinary teaching and research, coupled with academic rigor. By transcending disciplinary boundaries, we encourage our students, faculty, and staff to tackle complex and vexing challenges facing modern societies at local, national, and global levels. Our programs are responsive to the changing needs of society and relevant to the goals of our students and the needs of the community. Our academic environment is rich in opportunities for independence and collaboration and reflective of the traditions of excellence, innovation, and leadership that the University aims to establish. This mission is achieved by:

Building a strong foundation in Sciences and Engineering. Providing the knowledge and skills essential for career and personal success. Integrating teaching, research, and service in ways that enhance the learning

experience. Supporting a dynamic environment where innovation, openness, and creativity

are fostered. Using advanced technologies to meet the changing educational needs and to establish

links with the global community. Establishing partnerships with business, industry, educational institutions, and

government agencies.

2. GRADUATE GENERAL ACADEMIC POLICIES & REGULATIONS

2.1. INTRODUCTION

University of Science and Technology at Zewail City (UST) offers graduate programs in sciences and engineering disciplines. These graduate programs accept applications from B.Sc. and M.Sc. degree holders. Applicants with B.Sc. degrees have the opportunity to apply for MEng and M.Sc. graduate programs. Applicants with M.Sc. degrees have the opportunity to apply for the Ph.D. program. Applicants have to satisfy admission criteria to be accepted in graduate programs at UST. These admission criteria are intended to confirm the applicant’s scientific calibre in the field in which he/she is pursuing the graduate degree. Applicants should complete the online application form by the deadline announced on the UST website. Certificates, letters of recommendation, personal statement, and research proposal (for Ph.D. applicants) should be attached with the application.


All applications are processed and assessed by the graduate program committee (GPC) and approved by the graduate studies committee (GSC). Both committees are formed by the Dean of Academic Affairs and the Executive President.

The university has the right to modify any academic requirements. Requirements and regulations changed by the university will be announced when necessary to all enrolled students. The students are responsible for being aware of all academic requirements and regulations stated in this part and in each specific field of study. The Graduate Academic Requirements & Curriculum apply to all students towards their MENG, M.Sc. and Ph.D. programs. Specific Program Requirements are described under each field of study. The following sections list the graduate degrees that are offered at UST and present requirements for successful completion of offered degrees.

2.2. MENG, M.SC. AND PH.D. GRADUATE DISCIPLINES

Field of Study MEng MSc PhD

Biomedical Sciences

Nano-Science

Physics

Mathematics

Cyber Security

Artificial Intelligence

Nanotechnology and Nanoelectronics Engineering

Environmental Engineering

2.3. TRANSITIONAL ARRANGEMENTS

Polices and Regulations stated in this handbook are to be implemented to the admitted students starting by the academic year 2018-2019. Enrolled students before 2018-2019 academic year are subject to the polices, regulations and amendments of 2013 bylaw.

2.4. MENG DEGREE

2.4.1. ADMISSION CRITERIA FOR MENG DEGREES

To be granted admission to the UST MEng graduate program, an applicant should satisfactorily meet all of the following criteria as minimum requirements for admission:

1. B.Sc. with a minimum GPA of 2.7 granted from an accredited institute or a minimum of 5 years of relevant professional experience in the field of environmental

engineering. 2. Completed online application form. Motivation letter and copies of certificates

and transcripts of previous university degrees should be attached with the application.

3. A graduate program committee (GPC) formed of the program faculty will thoroughly review all submitted applications and will notify accepted applicants after final approval by the Graduate Studies Committee (GSC).


4. The program Committee applies “Equal Opportunity Selection” to all applicants regardless of colour, gender, religion, disability or nationality.

5. Due to limited capacity and high caliber of applicants, meeting the minimum requirements might not grant admission into the MEng Graduate Program.

2.4.2. DURATION AND REQUIREMENTS FOR GRANTING MENG DEGREES

Granting the MEng degree is subject to the following conditions:

1. Student maintained enrolment in the program for at least 18 months from the date the student first enrolled in the program. Semesters when student was on pre-approved leave do not count towards the minimum duration of 18-months.

2. The maximum duration for obtaining the MEng degree is 36 months from the date of enrolment. Students who exceed this duration will have to submit a new application and pay an application processing fee.

3. Successful completion of 33 credit hours including 27 credit hours for coursework and 6 credit hours for a capstone project.

4. Successful completion of all coursework requirements with a GPA of not less than 2.7 in all courses.

5. Successful completion of a capstone project with a grade of B or higher. 2.4.3. COURSEWORK REQUIREMENTS FOR MENG GRADUATE PROGRAMS

1. Students in the MEng programs at UST have to complete coursework with a total of 27 credit hours. Students should register for at least 6 credits per semester until they complete the required coursework.

2. Students should complete all coursework requirements within a minimum of 18 months and a maximum of 36 months from the date they enrolled in the program.

3. A GPA of 2.7 or higher is required for passing a course.

4. Students are required to re-register in a course for only one more time upon the approval of the program director and the Dean of Academic Affairs. If they achieve a GPA lower than 2.7 in this course. Students may be required to withdraw from the program if they do not maintain a good academic standing.

5. Courses in the MEng programs are offered only during fall and spring semesters.

Courses are not offered during the summer semester.


2.4.4. MENG CAPSTONE PROJECT

For fulfilment of the requirements of the MEng degree, each MEng student should complete a capstone project on a topic related to his/her field of study. The scope and level of effort for the capstone project should be equivalent to 6 credit hours. Students may register for capstone project only after completing 18 credit hours of coursework. Faculty advisors will be assigned to supervise student's project work. At the beginning of the project, students should consult their advisors, provide the program director with the title and abstract of the capstone project, and propose the names of two expert examiners from Zewail City. External examiners with appropriate biographical information may be invited from outside Zewail City.

At the end of the capstone project, each student should deliver a comprehensive technical report documenting project objectives, methods, results, and conclusions. The student should submit the report to the appointed examiners. The student should also deliver an oral presentation to the examiners. The examiners will evaluate and assign separate grades for the report and presentation. The separate grades for the report and presentation will be used to compute the final grade for the project. The weight assigned by the examiners to the grade for the report should not be less than 60% of the total grade. The remaining weight should be assigned to the oral presentation. Examiners may ask students to revise and resubmit their project reports. A single revision is allowed. If students fail to address examiners' comments and fail to achieve a minimum overall grade of B in the project, students may need to re-register for the capstone project and complete a new project.

2.5. M.SC. DEGREE

2.5.1. ADMISSION CRITERIA FOR M.SC. DEGREES

To be granted admission to the UST M.Sc. graduate program, an applicant should satisfactorily meet all of the following criteria as minimum requirements for admission:

1. B.Sc. with a minimum GPA of 3.0 or “Very Good” from an accredited national or international university/institute.

2. Proof of English language proficiency (TOEFEL iBT with a minimum score of 80, IELTS with a minimum score of 6.5, or pass English proficiency test at Zewail City).

3. Completed online application form. Motivation letter, letters of recommendation, and copies of certificates and transcripts of previous university degrees should be attached with the application.

4. A graduate program committee (GPC) formed of the program faculty will thoroughly review all submitted applications. The GPC may invite applicants to come to Zewail City for personal interviews and will notify accepted applicants after final approval by the Graduate Studies Committee (GSC).


5. The program committee applies “Equal Opportunity Selection” to all applicants

regardless of colour, gender, religion, disability or nationality

6. Due to limited capacity and high caliber of applicants, meeting the minimum requirements might not grant admission into the M.Sc. Graduate Program. The most qualified applicants will be selected.

2.5.2. DURATION AND REQUIREMENTS FOR M.SC. DEGREES

1. The minimum duration for obtaining an M.Sc. degree is 24 months from the date the student enrols in the program.

2. The maximum duration for obtaining the M.Sc. degree is 48 months from the date of enrolment.

3. Students must successfully complete 36 credit hours including 21 credit hours for

coursework and 15 credit hours for MSc thesis. 4. Students must successfully complete all coursework requirements within 12 to

24 months from date of enrolment. 5. Students have to attend graduate seminars for two full semesters. To pass the

graduate seminar courses, students have to attend at least 60% of the seminars held each semester. After each seminar, students who attended have to submit brief reports summarizing the topics discussed in the seminar. The program director will assign a faculty member to coordinate graduate seminar courses, monitor student attendance, receive and evaluate students' reports, and assign pass/fail grades to students.

6. Students should present his/her research results in a public seminar (maximum)

by the end of 39 months from the date of enrolment.

7. After 48 months of enrolment, students who did not complete degree requirements will be required to withdraw from the program. These students will have to submit a new application to re-enter the program with no guarantee that they will be accepted.

8. Students with excuses who fail to finish their program requirements within the

specified time frame might submit a petition to the Dean of Academic Affairs asking for an extension of 3 additional months. The Dean of academic affairs can approve the petition. Under extenuating circumstances, students have the right to obtain an extension for two times with a maximum of 6 months. Maternity leave(s) can be requested for a total of up to one year.

9. For determining the duration of enrolment in the M.Sc. program, semesters when students were on pre-approved leave for legitimate excuses will be excluded and will not count towards the maximum program duration of 48 months.


2.5.3. COURSEWORK REQUIREMENTS FOR M.SC. GRADUATE PROGRAMS 1. Students in the M.Sc. programs at UST have to complete coursework with a total

of 21 credit hours. Students should register for at least 9 credits per semester until they complete the required coursework.

2. Students should complete all coursework requirements within a minimum of 12 months and a maximum of 24 months from the date they enrolled in the program.

3. A GPA of 3.0 or higher is required for passing a course. Students are required to re-register in a course for only one more time upon the approval of the supervisor and the academic dean, if they achieve a GPA lower than 3.0 in this course. Students may be required to withdraw from the program if they do not maintain a good academic standing.

4. Courses in the M.Sc. programs are offered only during fall and spring semesters. Courses are not offered during the summer semester.

2.5.4. M.SC. THESIS

The graduate student together with his/her advisor, should provide the committee of graduate studies, with the title and abstract of the thesis work, and propose the names of three expert examiners from within Zewail City. External examiners with appropriate biographical information are welcome. The graduate studies committee should approve the initiation of the thesis defence process, the examination Panel which is composed of three examiners and assign a Chairperson of them. The Examination Panel Chairperson will invite the candidate to make an oral presentation highlighting the major achievements of the thesis, the conclusions which have been reached and the significance of the findings. Each examiner must provide an official letter stating his/her satisfaction with the quality of the written thesis, and with the candidate’s presentation and level of education. The panel members should, by consensus, confirm that the student is entitled to pass and can be granted the degree.

Based on the M.Sc. thesis, the student should have at least one published (or accepted for publication) article in the top 50% journals in the M.Sc. field where the top journals are determined based on Scopus/Web of Science journal rankings.


2.6. REGULATIONS FOR PH.D. DEGREES

2.6.1. PH.D. ADMISSION CRITERIA

To be granted admission to the Ph.D. graduate program, an applicant should satisfactorily meet all of the following criteria as minimum requirements for admission:

1. Passing the GRE exam with a minimum score according to the table below:

Major GRE TYPE Minimum required GRE

score

Biomedical Sciences General GRE 300 Nano- Science General GRE 300

Nanotechnology Engineering

General GRE 300

Basic Sciences General GRE* 300 Environmental Engineering General GRE 300

*In addition to the General GRE, a requirement for the following disciplines of: A Subject GRE of physics for Physics applicants: the minimum requirement for Physics subject GRE is 700. A Subject GRE of Math for Math applicants: the minimum requirement for Math subject GRE is 600.

2. Submission of three reference letters, two of which are obtained from professors familiar with the candidate’s work/academic performance. Internal supervisor, from Zewail City, may support the candidate with one of the three reference letters. If the applicant is a Zewail city graduate, the three letters may be from internal Zewail city faculty.

3. Submission of a written statement of purpose composed of a maximum of 1000 words highlighting interest in the graduate program, the proposed field of graduate study, and reasons for choosing University of Science and Technology at Zewail City.

4. The candidate must pass the “CANDIDACY EXAM”. The Ph.D. Candidacy Exam is a written comprehensive exam in the discipline of the Ph.D. It covers the contents in the compulsory courses of the program requirements in the M.Sc. study plan.

5. The applicant has only two trials to pass the CANDIDACY EXAM, which may be offered twice a year (exam time might differ as announced by the program).

6. The candidate will be formally accepted as a Ph.D. candidate after passing the candidacy examination and approving his/her doctoral plan.

7. Due to limited capacity and competitive competency, meeting the minimum requirements might not grant admission into the Ph.D. Graduate Program. The most qualified applicants will be selected.

8. Submission of online application form. Recommendation letters, personal statement, research proposal, and certificates and transcripts of previous university degrees should be attached with the application by the announced deadline.

9. A graduate program committee (GPC) formed of the program faculty will thoroughly review all submitted applications. The GPC may invite applicants to come to Zewail City for personal interviews and will notify accepted applicants after final approval by the Graduate Studies Committee (GSC).


2.6.2. DURATION OF PH.D. PROGRAM

Candidates who pass the Ph.D. candidacy exam should be able to complete the degree requirements in a minimum period of 30 months from date of passing the candidacy exam and a maximum period of 60 months from date of enrolment in PhD program. Maternity leave(s) can be requested for a total of up to one year.

Ph.D. candidates are required to attend on campus at least two days during the Ph.D. duration. 2.6.3. PH.D. SUPERVISION COMMITTEE

The student should join an advisor from Zewail City after the successful completion of the Candidacy Exam. The advisor would form a supervision committee from faculty in Zewail City and/or external faculty. This committee should be approved by the dean of academic affairs.

The committee is to advise the Ph.D. candidate of the status of the dissertation research, overall progress, approval of the general course requirements of the candidate’s major, and approval of the candidate dissertation proposal. 2.6.4. PH.D. REQUIREMENTS

The Ph.D. program at the University of Science and Technology is mainly research based with 60 credit hours of coursework and research that should be registered and covered during the course of the program within the duration indicated in section 2.6.2. These 60 credit hours are divided into 18 credits for coursework, 27 credits for directed research, and 15 credits for Ph.D. dissertation. For coursework, PhD candidates have to successfully pass six courses from among a pool of elective courses. At least four of the six courses have to be 700-level courses. Two of the six courses may be 600-level courses. All course selections have to be approved by supervisor. A GPA of 3.0 or higher is required for passing a course. If a student achieves a GPA lower than 3.0 in a course, he/she is required to re-register in this course for only one more time upon the approval of the supervisor and the academic dean. Students may be required to withdraw from the program if they do not maintain a good academic standing. The directed research is expected to cover the following material, but is not limited to it:

1. Mastering of research techniques. 2. Ability to critically review the literature. 3. Excellent research proposal with well-defined problem, clear hypothesis, original

idea, well-designed research in plan, good flow of ideas, and organization. 4. Submission of assignments on timely basis.


5. The supervisor might require the student to have a self-learning course or audit specific courses offered in graduate program at Zewail city, that would be counted as a directed research course. The course should cover selected topics in the field of the students Ph.D. program discipline. However, this course should be approved by the dean of academic affairs before starting it, where a justification for taking such course along with a syllabus with a clear assessment method should be submitted.

The assessment of the Directed Research course should be done by the supervision committee and it is based on monthly reports signed by the main supervisor and the student to keep track of the tasks, the progress, the challenges and a plan for the next month. By the end of each semester, a comprehensive report, signed by the whole supervision committee, should be submitted to the dean with the conclusion of passing or failing the course. The comprehensive reports of the Directed Research courses towards the end of the second year might be supported by tangible outcomes, such as Patenting inventions, copyright material, conference proceedings, book chapter, scientific paper preparation, submission or publication. In case the students need an extension to complete the tasks assigned to them in the Directed Research course, the committee might assign an In Progress grade (IP) with a justification report to be submitted to the dean office.

The 15 credits Ph.D. dissertation courses, require the students to complete their research work and the write up of their thesis under the mentorship of their research supervision committee. The students should be able to write all the components of the thesis in professional high-quality publication style. In case the students need an extension to complete the tasks assigned to them in a Ph.D. Dissertation course, the committee might assign an In Progress grade (IP) with a justification report to be submitted to the office of the Dean of Academic Affairs.

2.6.5. PH.D. DISSERTATION Upon completion of the above requirements, the Ph.D. candidate together with his/her advisor, should provide the committee with the title and abstract of the dissertation work, and propose the names of three expert examiners from Zewail City. External examiners with appropriate biographical information are welcome.

The Graduate Studies committee (GSC) should approve the thesis dissertation process, the examination panel of the examiners and select a chairperson among them.

Out of the Ph.D. thesis, the student should have at least two published (or accepted for publication) articles in the top 20% journals in the Ph.D. field or three published (or accepted for publication) articles in the top 50% journals in the Ph.D. field where the top journals are determined based on Scopus/Web of Science journal rankings.

2.6.6. DATA DEFENCE

Towards the end of the second year of enrolment into the program, the Ph.D. candidate is eligible to undertake a dissertation defence presentation, in which the data gathered so far is presented before the Ph.D. supervision committee for approval to continue the work.


In case of failure, the meeting might be repeated once more within a max of six months’ period, failure to pass this milestone will result in another meeting of the Graduate Studies Committee (GSC) with the supervision committee to make a decision regarding the Ph.D. candidacy termination.

2.6.7. PH.D. FINAL EXAMINATION COMMITTEE AND THESIS DEFENSE The Examining Panel Chairperson will invite the candidate to make an oral presentation highlighting the major achievements of the thesis research work, the conclusions which have been reached, and the significance of the findings. Each examiner must provide an official letter stating his/her satisfaction with the quality of the thesis, and with the candidate’s presentation and level of education.

2.6.8. GRADUATE ACADEMIC ADVISOR

Students in all programs will have a member of the Graduate Programs from the University of Science and Technology and/or Research Institutes assigned as graduate academic advisor. The role of the graduate academic advisor is to counsel the student in academic matters. In particular, the graduate academic advisor will ensure that the student adhere to the calendar and graduate studies regulations, help in selecting courses and provide guidance to the student in assembling the study plan throughout the foundation semester/year, and advising on the date of the candidacy exam. The graduate academic advisor should maintain contact with the student through regular meetings that are mutually agreed upon and should be accessible to the student to give advice and constructive criticism. 2.6.9. SCHOLARLY WORK DURING GRADUATE PROGRAM

The advisor or the graduate committee may choose to add additional requirements to obtain the M.Sc., MEng or Ph.D. degree, these requirements may include:

Attending graduate seminar regularly and periodically presenting about Ph.D.

research progress or alternate topics that could be assigned.

Teaching duties to both graduate M.Sc., MEng & Ph.D. students, including lab

supervision.

Participating in or presenting at international research conferences.

Writing research proposals or research grant applications.


3. ACADEMIC REGULATIONS

3.1. REGISTRATION

Graduate Students must register during the official registration period at the Department of Admissions & Registration at the times stated in the university academic calendar for each Fall/Spring Semesters. Students should consult their Graduate Academic Advisor and plan for the courses or for the thesis load during any semester; there should be minimal need for course changes after meeting the Graduate Academic Advisor. Change for courses can only take place during the first week of the regular semester. Deadline for dropping courses is stated in the university academic calendar and “WF” grade will be assigned to those whose performance is evaluated as less than “B” grade at the time of dropping the course. Students will receive a grade of “F” if they stop attending classes without dropping the course at the official time of dropping.

3.2. GRADUATE GRADING SYSTEM

The grades of any course are given at the end of each semester of the specific courses stated upon the recommendation of the Graduate Academic Advisor should be announced. The grade is the professor’s official estimate of the student’s achievement upon the final examination, assignments, and class participation. The following is the grading system for the Graduate Students. GRADES NOT INCLUDED IN THE GRADE POINT AVERAGE

P Pass

F Fail

I Incomplete

W Withdrew

WP Withdrawal Passing

WF Withdrawal Failing

IP In Progress

MAPPING GRADES

Grade Minimum Final % Points Level of Grade

A 90 % 4.0 Excellent

A- 85 % 3.7 Excellent

B+ 80 % 3.3 Very Good

B 75 % 3.0 Good

B- 70 % 2.7 Fair

C+ 65 %

C 60 %

F <60 % 0.0 Fail


3.3. GRADE POINT AVERAGE

The grade point average is calculated by adding all the quality points (Grade's equivalent points), then dividing the resulting sum over the total number of credit hours.

Credit = the unit of course credit hours Grade = the letter grade is the professor's official evaluation of the student

achievement, used for grade point average calculation Quality points = Grade X Credit

3.4. INCOMPLETE WORK

Graduate students who are unable to complete a course may be permitted to continue work in the course beyond the examination period. Any professor submitting an incomplete grade must submit an incomplete grade form to the Registrar’s office giving the following information:

Reasons for the incomplete work with approved justified emergency cases for not attending the final exam, only approved medical reports from the university clinic are accepted.

The missing material.

Actions necessary for removal of the incomplete.

In such a case a grade of “I”, for “incomplete” is assigned. The students must make arrangements with the professor to complete the course within maximum one semester from the next academic semester. Failure to complete the course after one academic semester, the grade in that course to be recorded as “F”, signifying failure.

If students have one incomplete grade, their academic load limit the following semester will not be affected. If they have more than one incomplete grade, the credit hours of the incomplete will be included in their academic load for the following semester.

Students who receive an incomplete grade(s) while on warning due to deficiency in their overall grade point average will not be allowed to register the following semester. If, however, they complete their incomplete work before the end of the late registration period, and are academically eligible, they will be allowed to proceed with registration.

3.5. OBTAINED GRADE

The Obtained Grade is the grade given by the course instructor after evaluating the Incomplete Work, to be recorded in the Incomplete Form and to be posted instead of the incomplete grade after the incomplete period deadline.

3.6. CHANGE OF GRADES

Final grades are the official grades given by the course instructor; they will not be changed unless there has been a reason. Only the faculty member can approve the change and state the reason in the “Change of Grade Form”. The change will become effective after the


approval of the faculty member, the program director and the Dean of Academic Affairs on the form and to be submitted to the Office of Admission and Registration.

3.7. REPEATING COURSE

Graduate Students who wish to repeat a course for which they have received a low grade may do that once for the purpose of improving the grade.

The higher grade of the course will appear on the transcript and will be counted in the grade point average toward degree requirements. The earlier grade is disregarded in the calculation of the grade point average.

Students may repeat one course with the Dean’s approval Students are normally allowed to repeat failed courses for both grade and credit.

However, the failing grade received for reasons of academic dishonesty will not be subject to repeat and will not be allowed to continue.

3.8. TRANSFER OF CREDITS Students from M.Sc. programs may transfer to the ZC-UST M.Sc. and MEng programs provided that they satisfy admission requirements for the M.Sc. and MEng program. Credits for courses from M.Sc. programs may be transferred to the M.Sc., MEng ZC-UST program provided that course syllabi are similar. Course instructors are required to examine the syllabi and indicate extent of similarity. Transfer of credit is subject to approval of the director of the program based on recommendation from course instructors. The accepted maximum credit transfer should not exceed 6 credits in the whole program.

3.9. WITHDRAWAL

3.9.1. WITHDRAWING FROM COURSES

Dropping all courses during the semester is considered “Withdrawal” and students are required to fill out a withdrawal form and to obtain all the required approvals before submitting the form to the Office of Admission and Registration.

Students who withdraw after the fifth week of the semester has started are required to submit a Drop/Add form with the instructor’s grade of WP/WF. The grade will be posted to the student academic transcript and no academic credit is given for courses graded with WP/WF...

Students who wish to return to the University after one or more semesters, will be required to fill out a readmission form, available in the office of Admission & Registration.

Readmission to the University is not granted automatically and will be subject to evaluation before approval.

3.9.2. WITHDRAWAL FROM THE UNIVERSITY

Students who wish to withdraw from the University for One Semester or more due to emergency circumstances are requested to fill in a “Withdrawal Form” and “Drop/Add Form”.


Students must obtain all approvals stated in the two “Forms”. Forms are available under “On Line” Forms.

Obtain signatures from Course Instructors /Program Advisors. Obtain signatures from Library and Accounts Office Submit the Forms to the Office of Admission and Registration Students may apply for “Readmission” if they wish to return after one

or more semester

3.10. DISMISSAL

Students who received an “F” grades are not allowed to continue the program. Student may

have the chance of repeating the course with the approval of the program director and the

Dean of Academic Affairs. Student is not allowed to repeat the grade of “F’ results from

Academic Dishonesty. Based on documented and accepted reasons, a student may be

dismissed from the university if he/she does not complete the required courses and thesis

within the period specified under the academic field of study.


4. PROGRAM THESIS Most Degrees Programs requires a thesis, requirements described of the individual programs, to be started after completing the course work successfully. The student is responsible for selecting and developing a thesis topic, with the consultation and recommendation of the graduate advisor. The student must submit the proposal to the graduate advisor for consideration. The student has to start the registration each semester for the Research Guidance course as offered by field of study. The thesis must be written in English and typed double-spaced, it will be judged on organization, content, documentation, and presentation. The thesis Advisor guide the student until submitting and presenting his/her thesis.

4.1. DISSERTATION / THESIS FORMAT Once the proposal has been defended and the supervisor(s) is (are) satisfied with the candidate's work, candidates should seek guidance from their supervisors regarding the use of a style manual appropriate to the academic discipline in which they are working.

4.2. PREPARATION FOR SUBMISSION OF THESIS The supervisor(s) is (are) responsible for advising and assisting the candidate to prepare for the submission of the thesis. The supervisor(s) is(are) responsible for ensuring, within reasonable limits, that the thesis presented to the internal and external examiners is of an acceptable standard and quality for the degree sought. It is the candidate's responsibility to prepare, assemble and distribute all materials in all copies of the thesis. The supervisor(s) shall evaluate the thesis in order to determine whether it is ready to proceed to the oral defence stage. The supervisor(s) shall advise the Dean of Academic Affairs (after this referred to as Dean) in writing immediately after a positive decision is reached so that the oral defence examination can be scheduled. The supervisor's notification will also indicate the definitive title of the thesis and will also propose the names of three external examiners with appropriate biographical information.

4.3. PREPARATION FOR THE ORAL DEFENCE It is the responsibility of the supervisor(s) to inform the candidate in a timely way of all requirements pertaining to the oral defence and to provide advice and support to the candidate in preparing for the oral defence. For the thesis defence all arrangements are made by the Dean’s Office after receiving the Intent to Submit Draft Thesis form from the candidate and the copies of the draft thesis. It is the responsibility of the Dean's Office to ensure that the candidate's academic file is complete and up- to- date, with all the requirements for the degree clearly indicated.

THE ORAL DEFENCE The Examining Panel Chairperson will invite the candidate to make an oral presentation, highlighting the major issues dealt with in the thesis, the conclusions which have been reached and the significance of the findings. This oral presentation should not exceed 20


minutes. All members of the Examining Panel are expected to ask probing questions on the methodology and the contents of the thesis and/or on the Research field.

SUBMISSION OF THESIS

It is the responsibility of the candidate to make all revisions and corrections to the thesis as required by the Examining Panel. The research supervisor (s) is (are) expected to advise the candidate in making these changes and to verify that they have been made. If members of the Examining Panel have withheld their signatures on the certification page at the time of the oral defence, it is the responsibility of the research supervisor (s) to ensure that they see the changes and indicate, on behalf of the Examining Panel, that they deem the changes to be complete and appropriate. The Examiners who are responsible for checking the corrections must provide an official letter stating their satisfaction on the corrections made.

DEPOSITION OF THE THESIS/DISSERTATION IN THE LIBRARY

The student should deposit his/her final copy of the Thesis/Dissertation in the library. The student should submit a soft copy of the thesis/dissertation. The soft copy should be saved as a PDF file. Awarding of the degree is pending submitting the thesis/dissertation in the library. A letter of confirmation should be sent to the office of the Registration to clear graduation. The student should approve a release statement to the Library.

5. STUDENT ACADEMIC RECORD & TRANSCRIPTS

5.1. STUDENT’S ACADEMIC RECORD All Academic Standing are recorded on the student’s academic record.

TRANSCRIPT

Students are entitled to one free transcripts when they graduate or withdraw from the university in a good standing. Current Students are not allowed to request transcripts during the registration & grading process.

ACADEMIC LOAD

The University has established the minimum and the maximum load of credits for graduate students to satisfy the registration requirements for their MEng, M.Sc. and Ph.D. programs. The maximum approved Course load is (13) credit hours per semester. A Student enrolled for (9) credit hours or more is considered to be a full-time graduate. A Student enrolled for less than (9) credit hours is considered part time.


6. CODE OF CONDUCT AT UNIVERSITY OF SCIENCE & TECHNOLOGY

6.1. IMPETUS

University of Science and Technology (UST) aims to establish an academic environment that reflects the highest academic ethical standards. UST students should conduct themselves according to ideals portrayed by “Egypt’s National Scientific Renaissance project”.

To ensure that the University can dedicate itself fully to its academic and educational vision, it is expected that of the UST community members will:

Be Truthful and forthright.

Reflect individual’s personal integrity in honest and responsible actions.

Disengage from behaviour that endangers their own sustained effectiveness or that has serious ramifications for their own safety, welfare, academic well-being or professional obligations, or for that of others.

Respect the general resources and physical property of the University. Such resources are assets in which community members have a vested interest, as these resources specifically support the institutional mission.

Treat other community members with civility and respect, recognizing that disagreement and informed debate are valued in an academic community.

6.2. STUDENT RIGHTS WITHIN THE UNIVERSITY COMMUNITY

With the approval of the Board of Directors, the University affirms the following student right and privileges:

The right to pursue academic goals without being subject to discrimination on the basis of race, colour, religion, ethnicity, geographic origin, age, sex, marital status or handicapped status.

Freedom to join organizations, to speak freely, to engage in discussion, to make inquiries, to exchange thought and opinion, to publish and exchange findings and recommendations, to speak, write, or print freely on any subject, and to sponsor speakers of their choice, subject only to the right of the University to make reasonable rules and regulations related thereto

To associate with whomsoever, they please.

To engage in the educational process.

To be secure in their persons, living quarters, papers, and effects from unreasonable, illegal, or unauthorized searches and seizures


6.3. ACADEMIC INTEGRITY POLICY The University assumes as a basic and minimum standard of conduct in academic matters that students be honest and that they submit for credit only the products of their own efforts. Both the ideals of scholarship and the need for fairness require that all dishonest work be rejected as a basis for academic credit. Students must refrain from any and all forms of dishonourable or unethical conduct related to their academic work such as:

a. PLAGIARISM

Submitting material that is in part or whole is not entirely one’s own work without properly citing sources.

Plagiarism includes, but is not limited to: Submitting a copied piece of writing as original work. The quotation or usage of another person’s words, ideas, opinions, thoughts,

or theories (even if paraphrased into one’s own words) without acknowledgment of the source.

The quotation or usage of facts, statistics, or other data or materials (including images) that are not clearly common knowledge without acknowledgment of the source.

b. FABRICATION

Inclusion of falsified, invented, or fictitious data or information that was not gathered in accordance with standard guidelines in an academic work.

Fabrication includes, but is not limited to;

Citation of sources that were not used to prepare the academic work, in the bibliography or other references.

Concealment or distortion of the true nature, origin, or function of data used in academic work.

Unauthorized submission of an academic work prepared totally or in part by another

c. CHEATING

Cheating is defined as obtaining or attempting to obtain, or aiding another to obtain credit for work, or any improvement in evaluation of performance, by any dishonest or deceptive means.

Cheating includes, but is not limited to;

Copying or attempting to copy from others during an exam or on an assignment.

Communicating answers with another student during an exam.


Preprograming a calculator to contain answers or other unauthorized information for exams

Using unauthorized materials, prepared answers, written notes, or concealed information during an exam.

Allowing others to do an assignment or portion of an assignment for you.

Allowing another person to copy one’s own academic work—whether intentionally or recklessly.

The unauthorized collaboration with any other person on an academic exercise, including collaborating on a take-home or make-up academic exercise.

Taking an exam for another person or having someone take an exam for you.

d. OTHER ACADEMIC MISCONDUCT

Any other act that disrupts the educational process or provides a student with an academic advantage over another student.

Other Academic Misconduct includes, but is not limited to:

Falsification of records and official documents: Altering documents affecting academic records; forging signatures of authorization or falsifying information on an official academic document, grade report, letter of permission, petition, drop/add form, ID card, or any other official University document.

Entering any University building, facility, office, or other property, or accessing any computer file or other University record or storage for the purpose of obtaining the answers or solutions to an academic exercise or to change a grade.

Bribing another person to obtain an academic exercise, including answers to questions of an un-administered academic exercise.

The unauthorized possession, copying, distribution, sale, or other transfer of all or any part of an academic exercise, or the answers or solutions to an academic exercise, whether or not the exercise has been administered.

Posting of notes or other materials from a class (whether the student is enrolled in the class or not) on the Internet, whether or not for a fee, if the faculty member has expressly prohibited the posting of such materials.

Curricula and Study Plans 2017/2018 26

7. CURRICULA AND STUDY PLANS 2017/2018

7.1. M.SC. IN BIOMEDICAL SCIENCES (36 CR)

7.1.1. PROGRAM DESCRIPTION

The Master’s degree in Biomedical Sciences at the University of Science and Technology at Zewail City is 36 credit hours, two-year program that prepares students for academic and professional careers in biomedical sciences. The research-based curriculum builds on the unique resources and expertise at Zewail City. The curriculum provides core courses that cover the foundation of biomedical research, and prepares the students for advanced disciplines that include Molecular Cell Biology, Biological Chemistry, Computational Biology, Stem Cells and Regenerative Medicine, Microbiology and Immunology, Cancer Biology, and Medical Genetics and Genomics. Students will benefit from the advanced laboratories of the program faculty to perform their 12 credit hour thesis work in the specified concentrations. 7.1.2. PROGRAM EDUCATIONAL OBJECTIVES (PEOS)

The program objectives of the Master’s degree in Biomedical Sciences include the following: 1. Provide modern advanced curricula, and research-based education. Students will

master the skills appropriate for graduate level biomedical sciences. They will develop proficiency in concentration-specific material, expertise in research methods, and proficiency in communicating data through presentations and publications.

2. Implement research-based education to enable students to acquire knowledge of specific fields of biomedical sciences, to promote critical thinking skills and the ability to research, collect, and analyze data.

3. Prepare a research-based thesis to and develop an academic project to completion using higher cognitive skills of synthesis and integration of knowledge.

4. Prepare students for careers in biomedical sciences in academia and the industry. 7.1.3. STUDENTS OUTCOMES

Recognize the fundamental concepts in biomedical sciences and corresponding concentration areas. Apply learned knowledge and experimental skills to synthesise relevant research questions, critically evaluate research publications, and develop research protocols. Perform independent research projects, analyse data, and communicate results in meaningful and professional ways.

Apply biomedical science in creating new knowledge, distribution of knowledge, and leading professional teams.

Acknowledge ethical issues in biomedical science research and implement the ethical practices in all aspects of graduate and postgraduate applications.


7.1.4. COURSES CATEGORIES

Year; Semester

Course (Department, Number, Title)

Category (Cr)

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Year 1; Fall BMS 601, Molecular and Cellular Biology 3

Year 1; Fall BMS 602, Biology of Human Diseases 3

Year 1; Fall ENG 601, Scientific English Writing 3

Year 1; Fall BMS 690, Graduate Seminar

Year 1; Spring BMS 627, Structural and Functional Biology and Therapy

3

Year 1; Spring BMS 604, Biostatistics 3

Year 1; Spring BMS 6XX, Concentration specific course 3

Year 2; Fall BMS 6XX, Concentration specific course 3

Year 2; Fall BMS 699, M.Sc. thesis 3

Year 2; Fall BMS 699, M.Sc. thesis 3

Year 2; Spring BMS 699, M.Sc. thesis 3 Year 2; Spring BMS 699, M.Sc. thesis 3

Year 2; Spring BMS 699, M.Sc. thesis 3

Year 2; Spring BMS 690, Graduate Seminar

Totals 3 12 6 15

Total Credit Hours Required for Completion of the Program 36 Percentage 13.2% 31.6% 23.6% 31.6%

7.1.5. DEGREE REQUIREMENTS FOR M.SC. IN BIOMEDICAL SCIENCES

A total of 38 credits is required for the M.Sc. degree in Biomedical Sciences Program. This includes 5 hours of University requirements courses, 21 credit hours of program and concentration specific course work, and 12 credit hours of research lab work. Students should consult their advisor on a regular basis to ensure that the prerequisites for their university requirements, program requirements, concentration requirements, and electives are fulfilled.

Biomedical Sciences Program (38) University

Requirements (Cr)

Concentration Requirements ( (Cr) Research Requirements

(Cr)

Total (Cr)

3 18 15 36 13.2% 55.2% 31.6% 100%


7.1.6. UNIVERSITY REQUIREMENTS The aim of university requirements is to provide UST students with scientific English writing skills and scientific communication and discussion skills.

Compulsory Courses of University Requirements (5 Cr) Course Code Course Title Cr L P ENG 601 Scientific English Writing 3 3 - BMS 690 Graduate Seminar 0 Total Credits 3

7.1.7. PROGRAM REQUIREMENTS

The aim of program requirements is to provide M.Sc. students of biomedical sciences programs in UST with skills and knowledge essential to lead academic, research, and professional career in the field of Biomedical Sciences. Track requirements includes courses of basic knowledge essential to all graduate students of biomedical sciences programs such as cell and molecular biology, Biology of Human Diseases and biostatistics.

Compulsory Courses of Program Requirements (12 Cr)

Course Code Course Title Cr L P

BMS 601 Molecular and Cellular Biology 3 3 -

BMS 602 Biology of Human Diseases 3 3 -

BMS 627 Structural and Functional Biology and Therapy 3 3 -

BMS 604 Biostatistics 3 2 1

Total Credits 12


7.1.8. CONCENTRATION REQUIREMENTS

The program offers a specialty in one of seven concentrations, which requires successful completion of 12 credit hours of concentration specific courses and electives.

Courses of Concentration Requirements (Molecular and Cell Biology) The student should select 2 courses from the following list (total 6 Cr)


BMS 620 Neurobiology of Disease 3 3 -

BMS 621 Advanced Cancer Biology 3 3 -

BMS 616 Molecular Virology and viral pathogenesis 3 3 -

BMS 619 DNA repair and Genome instability 3 3

BMS 617 Selected Topics in Molecular and cellular Biology 3 3

BMS 618 Methods in Molecular Biology 3 3

Total Credits 6

Courses of Concentration Requirements (Microbiology and Immunology) The student should select 2 courses from the following list (total 6 Cr)


BMS 612 Advanced topics in Microbiology 3 3 -

BMS 613 Microbiology of Human Pathogens 3 3 -

BMS 614 Bacteriophage Biology 3 3 -

BMS 615 Cellular and Molecular Immunology 3 3

Total Credits 6

Courses of Concentration Requirements (Chemical Biology and Drug Design) The student should select 2 courses from the following list (total 6 Cr)


BMS 605 Applied molecular modeling 3 3 -

BMS 606 Molecular Thermodynamics 3 3 -

BMS 626 Modern synthetic protocols 3 3 -

BMS 615 Chemical Biology 3 3

Total Credits 6

Courses of Concentration Requirements (Cancer Biology) The student should select 2 courses from the following list (total 6 Cr)



BMS 622 Cancer Metabolism 3 3 -

BMS 619 DNA repair and Genome instability 3 3 -

BMS 623 Advanced Topics in Cancer Research 3 3 -

Total Credits 6


Courses of Concentration Requirements (Stem Cells and Regenerative Medicine) The student should select 2 courses from the following list (total 6 Cr)


BMS 608 Regenerative Medicine and Stem Cell Biology 3 3 -

BMS 609 Human Embryology and Developmental Biology 3 3 -

BMS 610 Translational Medical Sciences 3 3 -

BMS 694 Selected Topics in Regenerative Medicine 3 3 -

Total Credits 6

Courses of Concentration Requirements (Medical Genetics and Genomics) The student should select 2 courses from the following list (total 6 Cr)


BMS 624 Advanced human genome and disease 3 3 -

BMS 625 Genomic and proteomic techniques in research and diagnostics

3 3 -

BMS 619 DNA repair and Genome instability 3 3 -


Total Credits 6


7.1.9. STUDY PLAN FOR M.SC. IN BIOMEDICAL SCIENCES

7.1.9.1. YEAR 1 FOR BIOMEDICAL SCIENCES (TOTAL CREDITS: 18 CR)

BIOMEDICAL SCIENCES YEAR 1 / SEMESTER 1

COURSE CODE COURSE TITLE CR L P PREREQUISITE

BMS 601 Molecular and Cellular Biology

3 3

BMS 602 Biology of Human Diseases 3 3

ENG 601 Scientific English Writing 3 3

BMS 690 Graduate Seminar 0 0 Pass/Fail

TOTAL 9



BMS 627 Structural and Functional Biology and Therapy

3 3

BMS 604 Biostatistics 3 2 1

BMS 6XX/BMS 7XX

Concentration specific elective

3 3

TOTAL 9




BMS 6XX Concentration specific elective

3 3

BMS 699 M.Sc. thesis 3 3


TOTAL 9






BMS 690 Graduate Seminar 0 0 Pass/Fail

TOTAL 9


7.2. PH.D. IN BIOMEDICAL SCIENCES (60 CR) A total of 60 credits of practical work is required for the Ph.D. degree in Biomedical Sciences Program. Students should consult their advisor on a regular basis to ensure that the requirements for the Ph.D. directed research and the Ph.D. dissertation are fulfilled. 7.2.1. STUDY PLAN FOR PH.D. IN BIOMEDICAL SCIENCES The aim of coursework requirements is to provide students in the Ph.D. program in Biomedical Sciences with special advanced knowledge essential for completion of doctoral research. PhD candidates have to take at least four courses from the set of 700-level courses in the course catalog. Students have to take an additional two courses; these two courses may be taken from the set of 700-level courses or may be taken from 600-level courses offered in the M.Sc. program in Biomedical Sciences. Ph.D. candidates may not register for 600-level courses that they have taken during the course of previous studies before being admitted to the Ph.D. program.

7.2.2. STUDY PLAN FOR PH.D. IN BIOMEDICAL SCIENCES




BMS 7XX/6XX BMS elective 1 3 3



TOTAL 9






TOTAL 9




BMS 703 Ph.D. Directed Research 9 9 Pass/Fail

TOTAL 9



BMS 703 Ph.D. Directed Research 9 9 Pass/Fail

TOTAL 9


7.2.2.3. YEAR 3 FOR BMS (TOTAL CREDITS: 18 CR)

Biomedical Sciences YEAR 3 / SEMESTER 1


BMS 799 Ph.D. Dissertation 9 9

TOTAL 9



BMS 799 Ph.D. dissertation 3 3



TOTAL 9






TOTAL 6


7.3. M.SC. IN NANO- SCIENCE (36 CR)


Nano- Science Master Program focuses on the scientific phenomena that occur at the nano-size range. In addition to giving a solid foundation in basic sciences that provide students with a unique and versatile set of theoretical skills, the program prepares the students for performing good nanoscience-related research, particularly in nanomedicine and functional materials fields. The program includes a total of 36 credits towards the M.Sc. degree in Nanoscale Science. 7.3.2. PROGRAM EDUCATIONAL OBJECTIVES (PEOS)

The Nano- Science Program aims at providing its graduates with: 1. Providing the Nanoscale Science students with a rigorous background in basic science,

mainly, chemistry and physics -as well as the enough knowledge related to understanding the nano-phenomenon, and the behaviour of materials and devices at the nanoscale.

2. Providing the Nano- Science students with the advanced skills in the design, development and characterization of new classes of nanomaterials.

3. Preparation of the Nano- Science students to receive individualized and team-based learning in nanoscience-related fields.

4. Conferring the scientific, personal and professional skills required for the graduates of the Nano- Science program towards excellent career in academia, research, education, governmental sector, industry and entrepreneurship.

7.3.3. STUDENTS OUTCOMES

Student Outcomes (SO)

1

The Nano- Science students will build up a solid knowledge in basic science, mainly chemistry and physics along with enough background that enable them to understand the behaviour as well the interactions of materials and devices at the nanoscale.

2 Providing practical skills in synthesis, characterization and finding appropriate applications for the synthesized nanomaterials.

3 Using several characterization tools such as the physical or chemical techniques for the morphological identifications.

4 Finding a way for implementing the developed nanoscience-based materials in the various application



Year; Semester


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Year 1; Fall NANOSC 601: Introduction to Nanoscience and Technology 3

Year 1; Fall NANOSC 602: Advanced Organic Chemistry 3

Year 1; Fall NANOSC 6XX (Elective 1) 3


Year 1; Spring NANOSC 690, Graduate Seminar -

Year 1; Spring NANOSC 603, Advanced Polymer Chemistry 3

Year 1; Spring NANOSC 604: Advanced Characterization Techniques 3

Year 2; Spring NANOSC 6XX(Elective 2) 3

Year 2; Fall NANOSC 699, M.Sc. thesis 3



Year 2; Fall NANOSC 690, Graduate Seminar -

Year 2; Spring NANOSC 699, M.Sc. thesis 3

Year 2; Spring NANOSC 699, M.Sc. thesis 3

Totals 3 18 15

Total Credit Hours Required for Completion of the Program 36

Percentage 8% 50% 42%

7.3.5. DEGREE REQUIREMENTS FOR M.SC. IN NANO- SCIENCE

A total of 36 credits is required for the M.Sc. degree in Nanoscale Science Program. This includes 18 credit hours of program compulsory and elective course work, 3 credit hours of university requirement courses and seminars; and 15 credit hours of research work. Students should consult their advisor on a regular basis to ensure that the prerequisites for their university requirements, program requirements, field requirements, and electives are fulfilled.

Nano- Science Program (38) University

Requirements (Cr)

Concentration Requirements (Math, Basic Sciences, Internship & Senior

Design Projects) (Cr)

Research Requirements

(Cr)

Total (Cr)

3 18 15 36 8% 50% 42% 100%


7.3.6. UNIVERSITY REQUIREMENTS

The aim of university requirements is to provide UST students with scientific English writing skills and scientific communication and discussion skills.

Compulsory Courses of University Requirements (5 Cr) Course Code Course Title Cr L P ENG 601 Scientific English Writing 3 3 - NANOSC 690 Graduate Seminar - Total Credits 3


The aim of program requirements is to provide M.Sc. students of Nano-Science program in UST with skills and knowledge essential to synthesize, characterize and direct the obtained materials towards the appropriate application. Track requirements includes courses of basic knowledge essential to all graduate students of Nanoscale-science program such as introduction to Nanoscience, Organic and Inorganic Chemistry, Characterization techniques and Molecular Spectroscopy, etc.



NANOSC 601 Introduction to Nanoscience and Technology 3 3 -

NANOSC 602 Advanced Organic Chemistry 3 3

NANOSC 603 Advanced Polymer Chemistry 3 3 -

NANOSC 604 Advanced Characterization Techniques 3 3

Total Credits 12

Elective Courses of Program Requirements, The students should select 2 courses of (6 Cr)


NANOSC 605 Advanced Molecular Spectroscopy 3 3 -

NANOSC 606 Transition metals and main group chemistry 3 3 -

NANOSC 607 Nanomedicine 3 3

NANOSC 608 Nanomaterials for Biomedical Applications 3 3 -

NANOSC 609 Advanced Pulmonary Drug Delivery 3 3 -

NANOSC 610 Macromolecules 3 3 -

NANOSC 611 Catalysis 3 3 -

NANOSC 612 Self-assembly 3 3 -


7.3.8. STUDY PLAN FOR M.SC. IN NANO- SCIENCE

7.3.8.1. YEAR 1 FOR NANO- SCIENCE (TOTAL CREDITS: 21 CR)

NANO- SCIENCE YEAR 1 / SEMESTER 1

COURSE CODE COURSE TITLE CR L P PREREQUISI

TE

NANOSC 601 Introduction to Nanoscience and Technology 3 3

NANOSC 602 Advanced Organic Chemistry 3 3

NANOSC 6XX Elective (1) 3 3


TOTAL 12


COURSE CODE COURSE TITLE CR L P PREREQUISI

TE

NANOSC 603 Advanced Polymer chemistry 3 3

NANOSC 604 Advanced Characterization Techniques 3 3

NANOSC 6XX Elective (2) 3 3

NANOSC 690 Graduate Seminar - - Pass/Fail

TOTAL 9

7.3.8.2. YEAR 2 FOR NANO- SCIENCE (TOTAL CREDITS: 15 CR)


COURSE CODE COURSE TITLE CR L P PREREQUISIT

E

NANOSC 699 M.Sc. thesis 3 3



NANOSC 690 Graduate Seminar - - Pass/Fail

TOTAL 9


COURSE CODE COURSE TITLE CR L P PREREQUISIT

E



TOTAL 6


7.4. PH.D. IN NANO- SCIENCE (60 CR)

A total of 60 credits of practical work is required for the Ph.D. degree in Nano-Science Program. Students should consult their advisor on a regular basis to ensure that the requirements for the Ph.D. directed research and the Ph.D. dissertation are fulfilled. 7.4.1. COURSEWORK REQUIREMENTS The aim of coursework requirements is to provide students in the Ph.D. program in Nano-Science with special advanced knowledge essential for completion of doctoral research. PhD candidates have to take at least four courses from the 700-level courses in the Course Catalog. Students have to take additional two courses; these two courses may be taken from the set of 700-level courses in the Course Catalog or may be taken from 600-level courses offered in the M.Sc. program in Nano-Science. Ph.D. candidates may not register for 600-level courses that they have taken during the course of previous studies before being admitted to the Ph.D. program.

7.4.2. STUDY PLAN FOR PH.D. IN NANO-SCIENCE

7.4.2.1. YEAR 1 FOR NANO-SCIENCE (TOTAL CREDITS: 18 CR)

Nano-Science YEAR 1 / SEMESTER 1


NANOSC 7XX/6XX NANOSC elective 1 3 3

NANOSC 7XX/6XX NANOSC elective 2 3 3

NANOSC 7XX NANOSC elective 3 3 3

TOTAL 9






TOTAL 9




NANOSC 701 Ph.D. Directed Research 9 9 Pass/Fail

TOTAL 9




TOTAL 9






TOTAL 9



NANOSC 799 Ph.D. dissertation 3 3



TOTAL 9






TOTAL 6


7.5. M.SC. IN PHYSICS (36 CR)


The program is designed for students who wish to train as physicists with an interest in High Energy Particle Physics, Astrophysics or Biological applications. Aspects of Physics, such as quantum field theory and advanced theories in particle physics are emphasized for those who are interested in the particle physics and Astrophysics. Aspects of Physics, such as Statistical Mechanics, Optics and Spectroscopy, with increasing importance in Biology, are emphasized for those students who are interested in Biophysics. In addition, the program prepares the student for collaborative interdisciplinary research at the interface with Molecular Biology, Biochemistry and Biophysics. Program scope: These program spans one of more of the following general areas:

The Fundamental Canon of contemporary Physics: Mechanics, Electromagnetism, Quantum Mechanics, and Statistical Mechanics

Mathematical and Computational methods and their practical implementation to solve physical and biophysical problems depending on the area of interest.

7.5.2. PROGRAM EDUCATIONAL OBJECTIVES (PEOS)

The Physical Science Program aims at providing its graduates with: 1. Providing the Physics students with a rigorous background in basic physics, mainly, classical physics and quantum physics as well as the enough knowledge related to understanding the high energy physics theories, astrophysics or biophysics, and its applications. 2. Providing the Physical science students with the advanced skills in the analytical and computational analysis. 3. Preparation of the Physical science students to receive individualized and team-based learning in high energy, astrophysics/biophysics-related fields. 4. Conferring the scientific, personal and professional skills required for the graduates of the Physical Science program towards excellent career in academia, research, education, governmental sector, industry and entrepreneurship.


Student Outcomes (SO) 1 Ability to apply fundamental concepts of high energy physics, astrophysics

and biophysics to formulate and solve complex problems in different scientific and basic research fields.

2 Ability to select and apply appropriate analytical and computational methods for modelling complex scientific problems in the fields of high energy, astrophysics or biophysics.

3 Ability to apply physical reasoning to analyse and solve problems in the three different areas.

4 Ability to Communicate effectively using mathematical and physical concepts with the engineering and scientific communities and with the society at large.

5 Ability to employ SW packages such as Mathematica, Matlab, Minitab, etc.



Year; Semester


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Year 1; Fall PHY 607, CLASSICAL MECHANICS 3

Year 1; Fall PHY 601, QUANTUM MECHANCIS I 3

Year 1; Fall PHY 6XX, PHY elective 1 3


Year 1; Spring PHY 690, Graduate Seminar -

Year 1; Spring PHY 602, QUANTUM MECHANICS II 3

Year 1; Spring PHY 603, ELECTRODYNAMICS I 3

Year 1; Spring PHY 6XX, PEU elective 2 3

Year 2; Fall PHY 699, M.Sc. thesis 3



Year 2; Fall PHY 690, Graduate Seminar -

Year 2; Spring PHY 699, M.Sc. thesis 3

Year 2; Spring PHY 699, M.Sc. thesis 3

Totals 3 18 15

Total Credit Hours Required for Completion of the Program

Percentage 8% 50% 42%

7.5.5. DEGREE REQUIREMENTS FOR M.SC. IN PHYSICS

A total of 36 credits is required for the M.Sc. degree in Physics of Earth and Universe Program. This includes 18 credit hours of program compulsory and elective course work, 3 credit hours of university requirement courses and seminars; and 15 credit hours of research work. Students should consult their advisor on a regular basis to ensure that the prerequisites for their university requirements, program requirements, field requirements, and electives are fulfilled.

Physics Program (36) University

Requirements (Cr)




(Cr)

Total (Cr)

3 18 15 36 8% 50% 42% 100%




Compulsory Courses of University Requirements (3 Cr) Course Code Course Title Cr L P ENG 601 Scientific English Writing 3 3 - PHY 690 Graduate Seminar - Total Credits 3


The aim of program requirements is to provide M.Sc. students of physics of earth and universe program in UST with essential skills and knowledge. Track requirements includes courses of basic knowledge essential to all graduate students of physics and astronomy program such as classical mechanics, quantum mechanics, and electrodynamics.

7.5.8. STUDY PLAN FOR M.SC. IN PHYSICS OF EARTH AND UNIVERSE

7.5.8.1. YEAR 1 FOR PHYSICS (TOTAL CREDITS: 21 CR)

PHYSICS YEAR 1 / SEMESTER 1


PHY 607 CLASSICAL MECHANICS 3 3

PHY 601 QUANTUM MECHANICS I 3 3

PHY 6XX PHY ELECTIVE 1 3 3


TOTAL 12



PHY 602 QUANTUM MECHANICS II 3 3

PHY 603 ELECTRODYNAMICS I 3 3

PHY 6XX PHY ELECTIVE 2 3 3

PHY 690 Graduate Seminar - Pass/Fail

TOTAL 9



PHY 601 QUANTUM MECHANCIS I 3 3 -

PHY 602 QUANTUM MECHANICS II 3 3

PHY 603 ELECTRODYNAMICS I 3 3 -

PHY 607 CLASSICAL MECHANICS 3 3

Total Credits 12





PHY 699 M.Sc. thesis 3 3



PHY 690 Graduate Seminar - - Pass/Fail

TOTAL 9

PHYSICS OF EARTH AND UNIVERSE YEAR 2 / SEMESTER 2




TOTAL 6

7.6. PH.D. IN PHYSICS (60 CR) A total of 60 credits of coursework, research, and dissertation writing is required for the Ph.D. degree in Physics. Students should consult their advisor on a regular basis to ensure that the requirements for the Ph.D. coursework directed research and the Ph.D. dissertation are fulfilled. 7.6.1. COURSEWORK REQUIREMENTS

The aim of coursework requirements is to provide students in the Ph.D. program in Physics with special advanced knowledge essential for completion of doctoral research. PhD candidates have to take at least four courses from the 700-level courses in the Course Catalog. Students have to take an additional two courses; these two courses may be taken from the set of 700-level courses in the Course Catalog or may be taken from 600-level courses offered in the M.Sc. program in Physics. Ph.D. candidates may not register for 600-level courses that they have taken during the course of previous studies before being admitted to the Ph.D. program.

7.6.2. STUDY PLAN FOR PH.D. IN PHYSICS


Physics YEAR 1 / SEMESTER 1


PHY 7XX/6XX PHY elective 1 3 3

PHY 7XX/6XX PHY elective 2 3 3

PHY 7XX PHY elective 3 3 3

TOTAL 9







TOTAL 9




PHY 701 Ph.D. Directed Research 9 9 Pass/Fail

TOTAL 9




TOTAL 9





TOTAL 9



PHY 799 Ph.D. dissertation 3 3



TOTAL 9






TOTAL 6


7.7. M.SC. IN MATHEMATICS (36 CR)


Mathematics is one of the crucial components that acts as a bridge between science, engineering and technology. The Mathematics program aims to provide scientists and engineers with solid mathematical foundations and modern mathematical tools to assist in modelling and understanding of real world problems. M.Sc. in Mathematics offers specialized study in four concentrations: Optimization and

Operational Research, Stochastic Modelling, Mathematical Analysis and Data Science.

Masters candidates must complete 12 credits of graduate-level core courses in the first one

and half year. In addition, with the guidance of the students’ supervisors, they must complete

9 credits from the concentrations elective courses such that 6 credits are from the same

concentration and the other 3 credits can be picked from the same or another concentration

that best fit their research.

Core Courses:

1. Advanced Computational Methods (3 Cr)

2. Introduction to Optimization (3 Cr)

3. Real Analysis (3 Cr)

4. Regression Analysis (3 Cr)

I. Optimization and Operational Research Elective Courses:

1. Convex Optimization (3 Cr)

2. Integer and Combinatorial Optimization (3 Cr)

3. Operations Research (3 Cr)

II. Stochastic Modelling Elective Courses:

1. Stochastic processes (3 Cr)

2. Time series Analysis (3 Cr)

3. Design of Experiments (3 Cr)

III. Mathematical Analysis Elective Courses:

1. Functional Analysis (3 Cr)

2. Topology and Measure Theory (3 Cr)

3. Fractional Calculus (3 Cr)

4. Advanced Complex Analysis (3 Cr)

IV. Data Science Elective Courses:

1. Machine Learning and Data mining (3 Cr)

2. Big Data Analytics and Cloud Computing (3 Cr)

3. Artificial Neural Networks and deep Learning (3 Cr)


The Mathematics program aims at providing its graduates with: 1. A solid foundation in mathematics. 2. The ability to efficiently formulate and model engineering and scientific problems. 3. The ability to effectively use new emerging computational methods in problem solving. 4. The skills of applying the mathematical concepts and data analysis techniques in solving

the engineering and scientific problems. 5. The ability to employ SW packages to solve mathematical problems and to efficiently

integrate it with the in-house models developed by the researcher.



Student Outcomes (SO) 1 Ability to apply concepts of mathematics to formulate and solve complex

problems in different scientific and engineering fields. 2 Ability to select and apply appropriate computational methods for

modelling complex scientific and engineering. 3 Ability to apply mathematical reasoning to analyse and solve problems in

different contexts. 4 Ability to Communicate effectively using mathematical concepts with the

engineering and scientific communities and with the society at large. 5 Ability to employ SW packages such as Mathematica, Matlab, Minitab, etc.


Year; Semester


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Year 1; Fall MATH 601, Advanced Computational Methods 3

Year 1; Fall MATH 602, Introduction to Optimization 3


Year 1; Fall MATH 690, Graduate Seminar -

Year 1; Spring MATH 603, Real Analysis 3

Year 1; Spring MATH 605, Regression Analysis 3

Year 1; Spring MATH 6XX, Math elective 1 3

Year 2; Fall MATH 6XX, Math elective 2 3

Year 2; Fall MATH 6XX, M.Sc. elective 3 3

Year 2; Fall MATH 699, M.Sc. thesis 3 Year 2; Fall MATH 690, Graduate Seminar -

Year 2; Spring MATH 699, M.Sc. thesis 3



Totals 3 21 12


Percentage 8.3 %

58.4%

33.3%

7.7.5. DEGREE REQUIREMENTS FOR M.SC. IN MATHEMATICS

A total of 36 credits is required for the M.Sc. degree in Mathematics Program. This includes 21 credit hours of program compulsory and elective course work, (3) credit hours of university requirement courses and seminars; and 12 credit hours of research work. Students should consult their advisor on a regular basis to ensure that the prerequisites for their university requirements, program requirements, field requirements, and electives are fulfilled.


Mathematics Program

University Requirements

(Cr)




(Cr)

Total (Cr)

3 21 12 36 8.3% 58.4% 33.3% 100%



Compulsory Courses of University Requirements (3 Cr) Course Code Course Title Cr L P ENG 601 Scientific English Writing 3 3 - MATH 690 Graduate Seminar - Total Credits 3


The aim of program requirements is to provide M.Sc. students of Mathematics program in UST with skills and knowledge essential to Mathematicians. Track requirements includes courses of basic knowledge essential to all graduate students of Mathematics program such as Advanced Computational Methods, Introduction to Optimization, Real Analysis, Regression Analysis.

7.7.8. STUDY PLAN FOR M.SC. IN MATHEMATICS

7.7.8.1. YEAR 1 FOR MATHEMATICS (TOTAL CREDITS: 18 CR)

MATHEMATICS YEAR 1 / SEMESTER 1


MATH 601 Advanced Computational Methods

3 3

MATH 602 Introduction to Optimization 3 3


MATH 690 Graduate Seminar - - Pass/Fail

TOTAL 9



MATH 601 Advanced Computational Methods 3 3 -

MATH 602 Introduction to Optimization 3 3

MATH 603 Real Analysis 3 3 -

MATH 605 Regression Analysis 3 3

Total Credits 12


MATHEMATICS YEAR 1 / SEMESTER 2


MATH 603 Real Analysis 3 3

MATH 605 Regression Analysis 3 3

MATH 6XX Math elective 1 3 3

TOTAL 9


MATH YEAR 2 / SEMESTER 1




MATH 699 M.Sc. thesis 3 3

MATH 690 Graduate Seminar - - Pass/Fail

TOTAL 9

MATH YEAR 2 / SEMESTER 2





TOTAL 9


7.8. PH.D. IN MATHEMATICS (60 CR) A total of 60 credits of practical work is required for the Ph.D. degree in Mathematics Program. Students should consult their advisor on a regular basis to ensure that the requirements for the Ph.D. directed research and the Ph.D. dissertation are fulfilled. 7.8.1. COURSEWORK REQUIREMENTS The aim of coursework requirements is to provide students in the Ph.D. program in Mathematics with special advanced knowledge essential for completion of doctoral research. PhD candidates have to take at least four courses from the 700-level courses in the Course Catalog. Students have to take an additional two courses; these two courses may be taken from the set of 700-level courses in the Course Catalog or may be taken from 600-level courses offered in the M.Sc. program in Mathematics. Ph.D. candidates may not register for 600-level courses that they have taken during the course of previous studies before being admitted to the Ph.D. program.

700-Level Elective Courses: Network Analysis and Advanced Linear Programming (3 Cr) Dynamic Optimization and Optimal Control (3 Cr) Multiple Criteria Optimization (3 Cr) Number Theory and Cryptography (3 Cr) Advanced Functional Analysis (3 Cr) Harmonic Analysis (3 Cr) Applied Dynamical Systems (3 Cr) Measure theory II (3 Cr) Advanced Topics in Computational and Stochastic PDES (3 Cr) High Performance Computing (3 Cr) Probabilistic Models and Machine Learning (3 Cr) Graph-based Machine Learning (3 Cr)

7.8.2. STUDY PLAN FOR PH.D. IN MATHEMATICS 7.8.2.1. YEAR 1 FOR MATHEMATICS (TOTAL CREDITS: 18 CR)

Mathematics YEAR 1 / SEMESTER 1


MATH 7XX/6XX Mathematics Elective 1 3 3

MATH 7XX/6XX Mathematics Elective 2 3 3

MATH 7XX Mathematics Elective 3 3 3

TOTAL 9






TOTAL 9





MATH 701 Ph.D. Directed Research 9 9 Pass/Fail

TOTAL 9




TOTAL 9





TOTAL 9



MATH 799 Ph.D. Dissertation 3 3



TOTAL 9






TOTAL 6


7.9. M.SC. IN CYBER SECURITY (JOINT BETWEEN CIE AND MATH PROGRAMS) (36 CR)


The MSc Program in Cyber Security is a thesis-based program intended for students aspiring to acquire advanced technical knowledge and research skills in the field of Cyber Security. The program focuses on issues related to crimes committed using computer technology. Students will learn how to defend information systems from cyber-attacks, how to recover compromised systems, and how to architect secure systems. Topics of study include security architectures, network defence, data protection, risk management, and cutting-edge blockchain and cryptotechnologies. The program includes a total of 36 credits towards the M.Sc. degree in Cyber Security. 7.9.2. PROGRAM EDUCATIONAL OBJECTIVES (PEOS)

The MSc Program in Cyber Security aims to achieve the following: 1. Equip graduates with advanced technical knowledge and skills to protect and defend

computer systems and networks and to address present and future challenges in Cyber security.

2. Develop graduates that can plan, implement, and monitor cyber security mechanisms to help ensure the protection of information technology assets.

3. Develop graduates that can identify, analyze, and remediate computer security breaches. 7.9.3. STUDENTS OUTCOMES


1 Ability to analyze and evaluate the cyber security needs of an organization.

2 Ability to design, develop and deploy effective solutions to real-world cyber security challenges

3 Ability to assess and manage security risks in modern business environments

4 Ability to design operational and strategic cyber security strategies and policies.


Year; Semester


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Year 1; Fall CIE 601, Computer Security 3

Year 1; Fall CIE 602, Network Security 3


Year 1; Fall CIE 690, Graduate Seminar -

Year 1; Spring CIE 603, Cryptography 3

Year 1; Spring CIE 604, Cryptography 3

Year 1; Spring CIE 6XX, (CIE Elective 1) 3


Year; Semester


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Year 2; Fall CIE 6XX, (CIE Elective 2) 3


Year 2; Fall CIE 699, M.Sc. thesis 3


Year 2; Spring CIE 699, M.Sc. thesis 9

Totals 3 21 12

Total Credit Hours Required for Completion of the Program 36

Percentage 8.33

% 58.4

% 33.3

% 7.9.5. DEGREE REQUIREMENTS FOR M.SC. IN CYBER SECURITY

A total of 36 credits is required for the M.Sc. degree in Cyber Security Program. This includes 21 credit hours of program compulsory and elective course work, 3 credit hours of university requirement courses and seminars; and 12 credit hours of research work. Students should consult their advisor on a regular basis to ensure that the prerequisites for their university requirements, program requirements, field requirements, and electives are fulfilled.

Cyber Security Program (36) University

Requirements (Cr)




(Cr)

Total (Cr)

3 21 12 36 8.3% 58.4% 33.3% 100%




Compulsory Courses of University Requirements (5 Cr) Course Code Course Title Cr L P ENG 601 Scientific English Writing 3 3 - CIE 690 Graduate Seminar - - - Total Credits 3


The aim of program requirements is to provide M.Sc. students of Cyber Security program in UST with skills and knowledge essential to synthesize, characterize and direct the obtained materials towards the appropriate application. Track requirements includes courses of basic knowledge essential to all graduate students of Cyber Security science program such as Introduction to Cyber Security, Computer Security, Network Security, and Cryptography.



CIE 601 Computer Security 3 2 3

CIE 602 Network Security 3 2 3

CIE 603 Cryptography 3 2 3

CIE 604 Cyber Security Legal Aspects & Ethical Concerns 3 3 -

Total Credits 12

Elective Courses of Program Requirements, the students should select 3 courses of (9 Cr)

Course Code Course Title Cr L P CIE 605 Computer Forensics 3 2 3 CIE 606 Cloud Computing Security 3 2 3 CIE 607 Critical Infrastructure Protection 3 2 3 CIE 608 Software Reverse Engineering and Malware Analysis 3 2 3 CIE 609 Disaster Recovery and Business Continuity 3 2 3 CIE 610 Intrusion Detection Systems 3 2 3 CIE 611 Securing the Internet of Things 3 2 3 CIE 612 Data Hiding 3 2 3 CIE 620 Advanced Topics in Cybersecurity 3 3 -


7.9.8. STUDY PLAN FOR M.SC. IN CYBER SECURITY

7.9.8.1. YEAR 1 FOR CYBER SECURITY (TOTAL CREDITS: 18 CR)

CYBER SECURITY YEAR 1 / SEMESTER 1


CIE 601 Computer Security 3 2 3

CIE 602 Network Security 3 2 3

ENG 601 Scientific English Writing 3 3 -

CIE 690 Graduate Seminar - - - Pass/Fail

TOTAL 9



CIE 603 Cryptography 3 2 3

CIE 604 Cyber Security Legal Aspects & Ethical Concerns

3 3 -

CIE 6XX Elective (1) 3 3 -

TOTAL 9

7.9.8.2. YEAR 2 FOR CYBER SECURITY (TOTAL CREDITS: 18 CR)





CIE 699 M.Sc. thesis 3 - -


TOTAL 9






TOTAL 9

7.10. M.SC. IN ARTIFICIAL INTELLIGENCE (JOINT BETWEEN CIE AND MATH PROGRAMS)

(36 CR)


The MSc Program in Artificial Intelligence is a thesis-based program intended for students aspiring to acquire advanced technical knowledge and research skills in the field of Artificial Intelligence. The program prepares students for innovation in Artificial Intelligence, in two ways: firstly, the creation of innovative techniques and methods within the research area of Artificial Intelligence and, secondly, the application of these techniques and methods to create


innovative computer solutions. The program focuses on the different techniques of AI and their applications to solve real-world problems. AI techniques, Optimization techniques, machine learning and deep learning fundamentals are covered in the core courses while the different AI applications are addressed in the elective courses. AI applications include computer vision, medical image analysis, Bioinformatics, NLP, search engine, conversational agents, and speech processing. The program includes a total of 36 credits towards the M.Sc. degree in Artificial Intelligence. 7.10.2. PROGRAM EDUCATIONAL OBJECTIVES (PEOS)

The MSc Program in Artificial Intelligence aims to achieve the following: 1. Equip graduates with advanced technical knowledge and skills to develop a computer-

based system to address present and future challenges in Artificial Intelligence. 2. Develop graduates that can deal with and solve technological and scientific problems

through the research of AI. 3. Enable students to be creative in addressing and solving scientific and technological

problems through research in Artificial Intelligence. 7.10.3. STUDENTS OUTCOMES


1 Ability to carry out AI research in academic or R&D environments.

2 Ability to design, develop and deploy effective solutions to real-world Artificial Intelligence challenges

3 Ability to identify how AI techniques can provide intelligent solutions to IT problems in companies and organisations

4 Ability to recognize the ongoing need to acquire new knowledge, to choose appropriate learning strategies, and to apply this knowledge


Year; Semester


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Year 1; Fall CIE 631, Artificial Intelligence Techniques 3

Year 1; Fall MATH 602, Introduction to Optimization 3



Year 1; Spring CIE 632, Machine Learning Fundamentals 3

Year 1; Spring CIE 633, Deep Learning Fundamentals 3

Year 1; Spring CIE 6XX, (CIE Elective 1) 3



Year 2; Fall CIE 699, M.Sc. thesis 3


Year 2; Spring CIE 699, M.Sc. thesis 9

Totals 3 21 12


Year; Semester


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Total Credit Hours Required for Completion of the Program (36)

Percentage 8.3 %

58.4%

33.3%

7.10.5. DEGREE REQUIREMENTS FOR M.SC. IN ARTIFICIAL INTELLIGENCE

A total of 36 credits is required for the M.Sc. degree in Artificial Intelligence Program. This includes 21 credit hours of program compulsory and elective course work, 3 credit hours of university requirement courses and seminars; and 12 credit hours of research work. Students should consult their advisor on a regular basis to ensure that the prerequisites for their university requirements, program requirements, field requirements, and electives are fulfilled.

Artificial Intelligence Program (36) University

Requirements (Cr)




(Cr)

Total (Cr)

3 21 12 36 8.3% 58.4% 33.3% 100%




Compulsory Courses of University Requirements (3 Cr) Course Code Course Title Cr L P ENG 601 Scientific English Writing 3 3 - CIE 690 Graduate Seminar - - - Total Credits 3


The aim of program requirements is to provide M.Sc. students of Artificial Intelligence program in UST with skills and knowledge essential to synthesize characterize and direct the obtained materials towards the appropriate application. Track requirements includes courses of basic knowledge essential to all graduate students of Artificial Intelligence program such as Artificial Intelligence techniques, Introduction to Optimization, Machine learning and Deep learning Fundamentals.



CIE 631 Artificial Intelligence Techniques 3 2 3

MATH 602 Introduction to Optimization 3 3 -

CIE 632 Machine Learning Fundamentals 3 2 3

CIE 633 Deep Learning Fundamentals 3 2 3

Total Credits 12

Elective Courses of Program Requirements, the students should select 3 courses of (9 Cr)

Course Code Course Title Cr L P CIE 634 Natural Language Processing Fundamentals 3 2 3 CIE 635 Search Engine 3 2 3 CIE 636 Speech Processing 3 2 3 CIE 637 Dialogue and Conversational Agent 3 2 3 CIE 638 Deep Learning in Computer Vision 3 2 3 CIE 639 Medical Image Processing 3 2 3

CIE 640 Fundamentals of Bioinformatics and Computational Biology

3 2 3

CIE 641 Distributed Machine Learning 3 2 3 CIE 650 Advanced Topics in Artificial Intelligence 3 2 3


7.10.8. STUDY PLAN FOR M.SC. IN ARTIFICIAL INTELLIGENCE

7.10.8.1. YEAR 1 FOR ARTIFICIAL INTELLIGENCE (TOTAL CREDITS: 18 CR)

ARTIFICIAL INTELLIGENCE YEAR 1 / SEMESTER 1


CIE 631 Introduction to Artificial Intelligence 3 2 3

MATH 602 Introduction to Optimization 3 3 -



TOTAL 9



CIE 632 Machine Learning Fundamentals 3 2 3

CIE 633 Deep Learning Fundamentals 3 2 3

CIE 6XX Elective (1) 3 2 3

TOTAL 9

7.10.8.2. YEAR 2 FOR ARTIFICIAL INTELLIGENCE (TOTAL CREDITS: 18 CR)







TOTAL 9






TOTAL 9


7.11. M.SC. IN NANOTECHNOLOGY AND NANOELECTRONICS ENGINEERING (36 CR)


The program is designed for students who wish to train as nano-technology Engineers. Aspects of nanofabrication of devices, nano-device physics and modelling, nano-photonics and photonic devices and nano-scale analogue and digital are emphasized. Program scope: These program spans one of more of the following general areas:

Nanofabrication of devices, MEMS, PV,.. Nano device physics and modelling Nano photonics and photonic devices nm-scale analog and digital VLSI / EDA


The educational objectives of the proposed M.Sc. in Nanotechnology and Nanoelectronics Engineering Program are derived from the mission and objectives of Zewail City of Science and Technology. The following program educational objectives are oriented to supply the market demand for highly professional nanotechnology engineers working in MEMS, Fabrication, electronics, and optics.

1. Prepare post graduate students who are professional nanotechnology engineers who work as ideal examples for the next generations and raise the reputations of the city by their successful contributions to the engineering world.

2. Prepare post graduate students who apply principles of mathematics, physics, and electronics, and micro-fabrication engineering concepts to design innovative, inexpensive, feasible, and fully functional nano-systems.

3. Prepare post graduate students who take part in the development of the country’s nanotechnology systems and are able to work abroad at different scientific niches such as MEMS, micro-fabrication, electronics, and optics.

4. Prepare post graduate students who have the required independency and professionality to pursue their graduate studies.


Student Outcomes (SO) 1 An ability to apply the engineering design process to produce solutions that

meet specific needs with consideration for public health and safety, and global, cultural, social, environmental, economic, and other factors as appropriate to the discipline.

2 An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

3 An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgements, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.

4 An ability to recognize the ongoing need to acquire new knowledge, to choose appropriate learning strategies, and to apply this knowledge

5 An ability to function effectively as a member or leader of a team that establishes goals, plans tasks, meets deadlines, and creates a collaborative and inclusive environment.



Year; Semester Course (Department, Number, Title)

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Year 1; Fall NANENG 601, Advanced VLSI Design 3

Year 1; Fall NANENG 602, Advanced Electromagnetics and Light wave Propagation

3


Year 1; Fall NANENG 690, Graduate Seminar -

Year 1; Spring NANENG 605, Advanced Nanofabrication and MEMS 3

Year 1; Spring NANENG 603, Engineering Mathematics and Computational Methods

3

Year 1; Spring NANENG 604, Advanced Devices and Semiconductors

3

Year 2; Fall NANENG 6XX, Elective 1 3

Year 2; Fall NANENG 6XX, Elective 2 3

Year 2; Fall NANENG 699, M.Sc. thesis 3


Year 2; Spring NANENG 699, M.Sc. thesis 3



Totals 3 21 12


Percentage 8.3% 58.3

% 33.3

% 7.11.5. UNIVERSITY REQUIREMENTS


Compulsory Courses of University Requirements (3 Cr) Course Code Course Title Cr L P ENG 601 Scientific English Writing 3 3 - NANENG 690 Graduate Seminar - Total Credits 3


The aim of program requirements is to provide M.Sc. students of Nanotechnology and Nanoelectronics Engineering Program in UST with skills and knowledge essential to Nanotechnology Engineers and Researchers. Track requirements includes courses of basic knowledge essential to all graduate students of Nanoscale Engineering program such as Advanced Nanofabrication, Mems, Electromagnetics, Semiconductors and VLSI Design.


Required Courses for Nanotechnology Program (38 Cr)

Year; Semester Course Title Cr L P

Year 1; Fall NANENG 601, Advanced VLSI Design 3 3

Year 1; Fall NANENG 602, Advanced Electromagnetics and Lightwave Propagation

3 3

Year 1; Fall ENG 601, Scientific English Writing 3 3


Year 1; Spring NANENG 605, Advanced Nanofabrication and MEMS 3 3


3 3

Year 1; Spring NANENG 604, Advanced Devices and Semiconductors 3 3

Year 2; Fall NANENG 6XX, Elective 1 3 3


Year 2; Fall NANENG 699, M.Sc. thesis 3 3


Year 2; Spring NANENG 699, M.Sc. thesis 3 3



Total Credits 36


7.11.7. STUDY PLAN FOR M.SC. IN NANOTECHNOLOGY ENGINEERING

7.11.7.1. YEAR 1 FOR NANOTECHNOLOGY AND NANOELECTRONICS ENGINEERING (TOTAL CREDITS: 18

CR)

Nanotechnology and Nanoelectronics Engineering ProgramYEAR 1 / SEMESTER 1

YEAR; SEMESTER COURSE TITLE CR L P PREREQUISITE

Year 1; Fall NANENG 601, Advanced VLSI Design

3 3

Year 1; Fall NANENG 602, Advanced Electromagnetics and Light wave Propagation

3 3

Year 1; Fall ENG 601, Scientific English Writing

3 3

Year 1; Fall NANENG 690, Graduate Seminar

-

TOTAL 9



Year 1; Spring NANENG 605, Advanced Nanofabrication and MEMS

3 3


3 3

Year 1; Spring NANENG 604, Advanced Devices and Semiconductors

3 3

TOTAL 9


7.11.7.2. YEAR 2 FOR NANOTECHNOLOGY AND NANOELECTRONICS ENGINEERING (TOTAL CREDITS: 18

CR)




Year 2; Fall NANENG 6XX Elective 2 3 3

Year 2; Fall NANENG 699, M.Sc. thesis 3 3

Year 2; Fall NANENG 690, Graduate seminar

- -

TOTAL 9






TOTAL 9


7.12. PH.D. IN NANOTECHNOLOGY AND NANOELECTRONICS ENGINEERING (60 CR) A total of 60 credits of practical work is required for the Ph.D. degree in Nanotechnology and Nanoelectronics Engineering Program. Students should consult their advisor on a regular basis to ensure that the requirements for the Ph.D. directed research and the Ph.D. dissertation are fulfilled. 7.12.1. STUDY PLAN FOR PH.D. IN NANOTECHNOLOGY AND NANOELECTRONICS ENGINEERING The aim of coursework requirements is to provide students in the Ph.D. program in Nanotechnology and Nanoelectronics engineering with special advanced knowledge essential for completion of doctoral research. PhD candidates have to take at least four courses from the following set of 700-level courses. Students have to take an additional two courses; these two courses may be taken from the following set of 700-level courses or may be taken from 600-level courses offered in the M.Sc. program in Nanotechnology and Nanoelectronics. Ph.D. candidates may not register for 600-level courses that they have taken during the course of previous studies before being admitted to the Ph.D. program.

Elective Courses for Ph.D. Degree in Nanotechnology and Nanoelectronics Engineering (18 Cr)


NANENG 706 Selected topics in VLSI Design 3 3 -

NANENG 707 Selected topics in Electromagnetics and Light wave Propagation

3 3 -

NANENG 708 Selected topics in Nanofabrication and MEMS 3 3 -

NANENG 709 Selected topics in Devices and Semiconductors 3 3 -

NANENG 710 Selected Topics in Analog MOS VLSI Design 3 3 -

NANENG 711 Selected Topics in Electronic Nano-Devices 3 3 -

NANENG 712 Selected Topics in Digital MOS VLSI Design 3 3 -

NANENG 713 Selected Topics in Data Converters 3 3 -

NANENG 714 Selected Topics in RF design 3 3 -

NANENG 715 Selected Topics in Computer Architecture 3 3 -

NANENG 716 Selected Topics in Nanophotonics 3 3 -

NANENG 717 Selected Topics in Photonic Devices and Systems 3 3 -

NANENG 718 Selected Topics in Computational photonics 3 3 -

7.12.1.1. YEAR 1 FOR NANOTECHNOLOGYT AND NANOELECTRONICS ENGINEERING (TOTAL CREDITS: 18

CR)

Nanotechnology and Nanoelectronics Engineering YEAR 1 / SEMESTER 1


NANENG 7xx/6xx

NANENG elective 1 3

NANENG 7xx NANENG elective 2 3

NANENG 7xx NANENG Elective 3 3

TOTAL 9

Nanotechnology and Nano electronics Engineering YEAR 1 / SEMESTER 2


NANENG 7xx/6xx

NANENG elective 4 3




TOTAL 9

7.12.1.2. YEAR 2 FOR NANOTECHNOLOGY AND NANO ELECTRONICS ENGINEERING (TOTAL CREDITS: 18

CR)



NANENG 703 Ph.D. Directed Research 9 9 Pass/Fail

TOTAL 9




TOTAL 9


CR)




TOTAL 9



NANENG 799 Ph.D. dissertation 9 9 Pass/Fail

TOTAL 9


CR)




TOTAL 9


7.13. M.SC. IN ENVIRONMENTAL ENGINEERING (36 CR)


The M.Sc. program in Environmental Engineering is a thesis-based program intended for students aspiring to acquire advanced technical knowledge and research skills in the field of environmental engineering. Besides core courses delivering broad background, the M.Sc. program offers elective courses covering the following environmental engineering disciplines: water treatment and desalination, wastewater treatment, air quality, solid waste management, and soil and water management.


The M.Sc. program in environmental engineering aims to achieve the following: 1. Equip graduates with advanced technical knowledge to address present and future

challenges in environmental engineering disciplines. 2. Provide graduates with strong research skills to pursue careers in academia, cutting-

edge consulting, or industrial research and development. 3. Prepare graduates who contribute to sustainable development in their respective

employment sectors such as industry, consulting, and government agencies.


Student Outcomes (SO) 1 Ability to identify, formulate, and solve complex environmental

engineering problems by applying principles of engineering, science, and mathematics.

2 Ability to collect, analyse, and interpret laboratory and field data relevant to various environmental engineering disciplines.

3 Ability to analyse the fate and transport of substances in and between air, water, and soil phases as well as in engineered systems.

4 Ability to recognize the ongoing need to acquire new knowledge, to choose appropriate learning strategies, and to apply this knowledge



Year; Semester


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Year 1; Fall ENV 601, Environmental Sciences 3

Year 1; Fall ENV 602, Environmental Data Analysis and Statistics 3 Year 1; Fall ENV 603, Environmental Systems Modelling 3


Year 1; Spring ENV 604, Transport Phenomena Modelling 3

Year 1; Spring ENV 6XX, Elective 1 3


Year 1; Spring ENV 690, Graduate Seminar - Year 2; Fall ENV 699, M.Sc. thesis 3

Year 2; Fall ENV 699, M.Sc. thesis 3

Year 2; Fall ENV 699, M.Sc. thesis 3

Year 2; Fall ENV 690, Graduate Seminar -

Year 2; Spring ENV 699, M.Sc. thesis 3

Year 2; Spring ENV 699, M.Sc. thesis 3

Totals 3 18 15


Percentage 8% 50% 42% 7.13.5. UNIVERSITY REQUIREMENTS


Compulsory Courses of University Requirements (3 Cr) Course Code Course Title Cr L P


ENV 690 Graduate Seminar - - -

Total Credits 3



The aim of compulsory requirements is to provide students in the environmental engineering M.Sc. program with broad fundamental knowledge essential for the different environmental engineering disciplines. Students have to take the following four courses:

In addition to the compulsory courses above, students have to take two courses equivalent to 6 credit hours. Elective courses cover the following disciplines of environmental engineering: water treatment and desalination, wastewater treatment, air quality, solid waste management, and soil and water management. Students may select their elective courses from one or more disciplines.

Elective Courses for M.Sc. in Environmental Engineering (6 Cr)


ENV 605 Unit Operations in Environmental Engineering 3 3 -

ENV 620 Aquatic Chemistry 3 3 -

ENV 621 Environmental Microbiology 3 3 -

ENV 622 Soil Biology and Biogeochemical Cycles 3 3 -

ENV 623 Advanced Reaction Engineering 3 3 -

ENV 630 Remote-sensing and GIS Applications for Environmental Engineers

3 3 -

ENV 641 Water Treatment 3 3 -

ENV 642 Desalination Systems Design 3 3 -

ENV 643 Domestic Wastewater Treatment 3 3 -

ENV 644 Industrial Wastewater Treatment 3 3 -

ENV 650 Solid and Hazardous Waste Management 3 3 -

ENV 651 Cleaner Production and Waste Valorisation 3 3 -

ENV 660 Air Quality Measurements and Modelling 3 3 -

ENV 661 Air Pollution Control 3 3 -

ENV 680 Soil Water Processes in Agroecosystems 3 3 -

ENV 681 Soil Salinity and Land Reclamation 3 3 -

ENV 697 Selected Topics in Environmental Engineering 3 3 -



ENV 601 Environmental Sciences 3 3 -

ENV 602 Environmental Data Analysis and Statistics 3 3 -

ENV 603 Environmental Systems Modelling 3 3 -

ENV 604 Transport Phenomena Modelling 3 3 -

Total Credits 12


7.13.7. STUDY PLAN FOR M.SC. IN ENVIRONMENTAL ENGINEERING

7.13.7.1. YEAR 1 FOR M.SC. (TOTAL CREDITS: 21 CR)

M.SC. ENV ENG YEAR 1 / SEMESTER 1


ENV 601 Environmental Sciences 3 3 -

ENV 602 Environmental Data Analysis and Statistics

3 3 -

ENV 603 Environmental Systems Modelling

3 3 -


TOTAL 12



ENV 604 Transport Phenomena Modeling

3 3 -

ENV 6XX Elective 1 3 3 -

ENV 6XX Elective 2 3 3 -

ENV 690 Graduate Seminar - - - Pass\Fail

TOTAL 9

7.13.7.2. YEAR 2 FOR M.SC. (TOTAL CREDITS: 15 CR)



ENV 699 M.Sc. thesis 3 - -



ENV 690 Graduate Seminar - - - Pass/Fail

TOTAL 9





TOTAL 6


7.14. PH.D. IN ENVIRONMENTAL ENGINEERING (60 CR) A total of 60 credits of coursework, research, and dissertation writing is required for the Ph.D. degree in Environmental Engineering. Students should consult their advisor on a regular basis to ensure that the requirements for the Ph.D. coursework directed research and the Ph.D. dissertation are fulfilled. 7.14.1. COURSEWORK REQUIREMENTS

The aim of coursework requirements is to provide students in the Ph.D. program in environmental engineering with special advanced knowledge essential for completion of doctoral research. PhD candidates have to take at least four courses from the following set of 700-level courses. Students have to take an additional two courses; these two courses may be taken from the following set of 700-level courses or may be taken from 600-level courses offered in the M.Sc. program in environmental engineering. Ph.D. candidates may not register for 600-level courses that they have taken during the course of previous studies before being admitted to the Ph.D. program.

Elective Courses for Ph.D. Degree in Environmental Engineering (18 Cr)


ENV 710 Selected Topics in Environmental Sciences 3 3 -

ENV 715 Selected Topics in Environmental Data Analysis 3 3 -

ENV 720 Selected Topics in Water Treatment and Desalination 3 3 -

ENV 721 Selected Topics in Wastewater Treatment 3 3 -

ENV 725 Selected Topics in Water Resources Management 3 3 -

ENV 726 Selected Topics in Hydrology 3 3 -

ENV 727 Selected Topics in Municipal Hydraulics 3 3 -

ENV 730 Selected Topics in Computational Fluid Dynamics 3 3 -

ENV 731 Selected Topics in Environmental Fluid Dynamics 3 3 -

ENV 740 Selected Topics in Transport Phenomena 3 3 -

ENV 750 Selected Topics in Waste Valorization 3 3 -

ENV 760 Selected Topics in Air Quality Engineering 3 3 -

ENV 770 Selected Topics in Pollution Control 3 3 -

7.14.2. STUDY PLAN FOR PH.D. IN ENVIRONMENTAL ENGINEERING

7.14.2.1. YEAR 1 FOR ENVIRONMENTAL ENGINEERING (TOTAL CREDITS: 18 CR)

Environmental Engineering YEAR 1 / SEMESTER 1


ENV 7XX/6XX ENV elective 1 3 3

ENV 7XX/6XX ENV elective 2 3 3

ENV 7XX ENV elective 3 3 3

TOTAL 9







TOTAL 9




ENV 701 Ph.D. Directed Research 9 9 Pass/Fail

TOTAL 9




TOTAL 9





TOTAL 9



ENV 799 Ph.D. dissertation 3 3



TOTAL 9






TOTAL 6


7.15. MENG IN ENVIRONMENTAL ENGINEERING (33 CR)


The MEng program in Environmental Engineering is a non-thesis program intended for practicing professionals in the environmental engineering sector as well as recent graduates looking to acquire knowledge and skills to enter the environmental-engineering job market. The objective of this MEng program is to equip program participants with additional training and expertise required to advance their careers in one or more of the following environmental engineering fields: water quality and treatment, air quality and pollution reduction, sustainability and waste management, and assessment and selection of renewable energy systems. The courses in the MEng program are tailored to provide participants with applied knowledge rather than pure theoretical knowledge. Course instructors have extensive professional experience and work on transferring their expertise to program participants.


The educational objectives of the MEng program in environmental engineering are to: 1. Equip program participants with advanced technical knowledge to pursue and advance

careers in the environmental engineering professional practice 2. Prepare graduates at more specialized level to meet present and future challenges in

environmental engineering disciplines 3. Prepare graduates who contribute to sustainable development in their respective

employment sectors such as industry, consulting, and government agencies 4. Train graduates who can efficiently and productively use modern computational tools

and experimental methods.


Student Outcomes (SO) 1 Ability to identify, formulate, and solve complex engineering problems by

applying principles of engineering, science, and mathematics. 2 Capacity to apply the engineering design process to produce

environmental-engineering solutions and systems that meet specified needs with consideration for public health and safety, sustainability, uncertainty, risk, and global, cultural, social, environmental, and economic factors.

3 Ability to analyze the fate and transport of substances in and between air, water, and soil phases as well as in engineered systems.

4 Ability to collect, analyze, and interpret laboratory and field data relevant to various environmental engineering disciplines.

5 Ability to successfully work in and lead multidisciplinary teams.

Course Catalogue 2017/2018

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Year; Semester Course (Department, Number, Title)

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Year 1; Fall ENV 610, Fundamentals of environmental engineering

3

Year 1; Fall ENV 640, Water quality measurements and data analysis

3

Year 1; Fall ENV 650, Solid and Hazardous waste management 3

Year 1; Spring ENV 660, Air quality measurements and modeling 3

Year 1; Spring ENV 630, Remote sensing and GIS applications for environmental engineers

3


Year 2; Fall ENV 6XX, Elective 2 3



Year 2; Spring ENV 698, Capstone project 6

Totals - 27 6


Percentage - 82% 18%

7.15.5. COMPULSORY AND ELECTIVE COURSES

The aim of compulsory requirements is to provide students in the environmental engineering MEng program with broad knowledge essential for the different environmental engineering disciplines. Students have to take the following five courses:


Course Code

Course Title Cr L P

ENV 610 Fundamentals of environmental engineering 3 3 -

ENV 640 Water quality measurements and data analysis 3 3 -

ENV 650 Solid and Hazardous waste management 3 3 -

ENV 660 Air quality measurements and modeling 3 3 -

ENV 630 Remote sensing and GIS applications for environmental engineers

3 3 -

Total Credits 15


74

In addition to the compulsory courses above, students have to take four courses equivalent to 12 credit hours. Elective courses cover the following disciplines of environmental engineering: water treatment and desalination, wastewater treatment, air quality, solid waste management, and soil and water management. Students may select their elective courses from one or more disciplines.

Elective Courses for M.Sc. in Environmental Engineering (6 Cr)


ENV 631 Computational Methods for Engineers 3 3 -

ENV 632 Life Cycle Assessment and Costing 3 3 -

ENV 633 Environmental Risk Assessment 3 3 -

ENV 634 Environmental and Social Impact Assessment 3 3 -

ENV 642 Desalination Systems Design 3 3 -

ENV 645 Advanced Water and Wastewater treatment 3 3 -

ENV 651 Cleaner Production and Waste Valorization 3 3 -

ENV 661 Air Pollution Control 3 3 -

ENV 670 Integrated Water Resources Management 3 3 -

ENV 686 Renewable Energy Systems 3 3 -

ENV 687 Concentrated Solar Power and Photovoltaics 3 3 -

7.15.6. STUDY PLAN FOR MENG IN ENVIRONMENTAL ENGINEERING

7.15.6.1. YEAR 1 FOR MENG (TOTAL CREDITS: 18 CR)

MENG ENV ENG YEAR 1 / SEMESTER 1


ENV 610 Fundamentals of environmental engineering

3 3 -

ENV 640 Water quality measurements and data analysis

3 3 -

ENV 650 Solid and Hazardous waste management

3 3 -

TOTAL 9



ENV 660 Air quality measurements and modeling

3 3 -

ENV 630 Remote sensing and GIS applications for environmental engineers

3 3 -

ENV 6XX ENV elective 1 3 3 -

TOTAL 9


75

7.15.6.2. YEAR 2 FOR MENG (TOTAL CREDITS: 15 CR)






TOTAL 9



ENV 698 Capstone project 6 - 6 Successful completion of 27 credit hours of coursework

TOTAL 6


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8. COURSE CATALOG 2018/2019

8.1. COMMON COURSES CATALOG

ENG 601

Scientific English Writing Compulsory, Credits: 3 (3+0) The course aims to take the candidate through the journey of identifying and practicing basics and dynamics of effective scientific writing , integrating and mastering the suitable scientific collocations, idiomatic phrases and grammatical structures, defining the types of plagiarism and avoiding them through summarizing, paraphrasing, and synthesizing valid and reliable research articles using the discipline-related documentation style, enhancing improved self-editing, and deconstructing high-impact scientific manuscripts to identify the required language structures in each of them. These stages will assist in formulating a well-designed, concise research proposal.

8.2. GRADUATE PROGRAMS COURSE CATALOG

8.2.1. COURSE CATALOG FOR GRADUATE BMS PROGRAM

BMS 601

Molecular and Cellular Biology Compulsory, Credits: 3 (3+0) Prereq: The course aims at introducing students to DNA structure, replication, translation, DNA repair, telomeres, cell cycle, different signaling pathways and how they are linked to the cell cycle, cell death, autophagy and apoptosis. Text: Bruce Alberts and Alexander Johnson, Molecular Biology of the Cell & Watson et al, Molecular Biology of the Gene, Seventh Edition.

BMS 602

Biology of Human Disease Compulsory, Credits: 3 (3+0) Prereq: This course covers main concepts the process of disease evolution on the molecular level. By applying modern technologies in molecular biology and genetics, students will learn how the molecular pathways are disrupted in the disease process. Text: Essentials of Human disease 2nd Ed. Crowley ISBN 13: 9781449688431

Nussbaum; Thompson & Thompson Genetics in Medicine Supplementary material: https://www.biomedcentral.com/collections/diseasegenomics

BMS 603

Bioinformatics Elective, Credits: 3 (3+0) Prereq: The students will be introduced to designing of primers for multiple purposes (PCR, qPCR, Cloning), performing BLAST, analyzing DNA sequences and inferring the protein sequence from a DNA code. Students will also learn how to check the homology of a protein to other species and if it has paralogues or share domains/homology with other proteins. Students should be able to predict from the sequence if a protein has a Mitochondrial Targeting Sequence or Nuclear Localization Sequence. Text: Jin Xiong, Essential Bioinformatics.

tel:9781449688431

https://www.biomedcentral.com/collections/diseasegenomics


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BMS 627

Structural and Functional Biology and Therapy Compulsory, Credits: 3 (3+0) Prereq: This course covers the principles, methods, applications, and modern paradigm in structural biology, with emphasis on the recent concepts and advances in this fast growing field of science. It includes topics on nucleic acids and protein structure and dynamics as well as the structure-function relationship of other biologically important macromolecules. The course also deals with the approaches used to characterize biologically targeted molecules for the development of efficient therapies. It presents the benefits, limitations, and future directions of modern techniques of X-ray crystallography, mass spectrometry, and structure-based therapeutic design. Textbook: 1. Structural Biology in Drug Discovery: Methods, Techniques, and Practices, ISBN-13: 978- 1118681015, ISBN-10: 1118681010. 2. Textbook Of Structural Biology, ISBN-13: 978-9812772084, ISBN-10: 9812772081

BMS 604

Biostatistics Compulsory, Credits: 3 (3+0) Prereq: This course covers a broad range of basic statistical methods used in the health sciences. The course begins by covering methods of summarizing data through graphical displays and numerical measures. Basic probability concepts will be explored to establish the basis for statistical inference. Confidence intervals and hypothesis testing will be studied with emphasis on applying these methods to relevant situations. Both normal theory and nonparametric approaches will be studied including one- and two-sample tests of population means and tests of independence for two-way tables. Students will be introduced to one-way analysis of variance (ANOVA), correlation, and simple linear regression. The course focuses on understanding when to use basic statistical methods, how to compute test statistics and how to interpret and communicate the results. Computer applications are included as part of the course to introduce students to basic data management, reading output from computer packages, interpreting and summarizing results. Text: Sr. Ralph D'Agostino, Lisa Sullivan and Alexa Beiser, Introductory Applied Biostatistics.

BMS 605

Applied molecular modelling Elective, Credits: (3+0) Prereq: This course aims to consolidate students’ knowledge and skills in the fields of Molecular models; Molecular Dynamics and Monte Carlo approaches, visualization. This will be achieved through a set of seminars and hands-on experience in designing, customizing and use of programs to study small molecules and biomolecules. The course will introduce students to simulation techniques for their research. Text: D. Frenkel and B. Smit, Understanding molecular simulation. From algorithms to applications, Academic 2002. Supplementary text: A. R. Leach, Molecular modeling. Principles and applications, Pearson Education, 2009


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BMS 606

Molecular Thermodynamics Elective, Credits: 3 (3+0) Prereq: This is a course on molecular thermodynamics and statistical mechanics. It covers the concepts of entropy, enthalpy, heat capacity, free energy, ligand binding, solvation; the properties of water; the hydrophobic effect; solution electrostatics; adsorption; and physical and chemical kinetics. Text: Molecular Driving Forces: Statistical Thermodynamics in Biology, Chemistry, Physics, and Nanoscience, 2nd Edition, by Ken Dill and Sarina Bromberg

BMS 607

Chemical Biology Elective, Credits: 3 (3+0)

Prereq: This course will provide a high-level overview on the structure, function and chemistry of biological macromolecules including proteins, nucleic acids and carbohydrates. Topics include protein and nucleic acid folding, energetics of macromolecular interactions (kinetics and thermodynamics) and mechanistic enzymology. Using specific examples from the current literature, each topic will stress how chemists have used molecular level tools and probes to help understand the specific systems under study. The overarching theme in this course will be that structure and function are intimately linked. Text: Chemical Biology 1st Edition, by Herbert Waldmann

BMS 608

Regenerative Medicine and Stem Cell Biology Elective, Credits: 3 (3+0) Prereq: This course covers the fundamentals of stem cell biology: concepts, methodologies and research. The material covers different types of stem cell, embryonic, adult, fetal and stem cell reprogramming. Topics include stem cells in health and daises, genetics and epigenetics in stem cells, application of stem cell therapy in human diseases, and modern techniques and approaches in regenerative medicine. Text: Essentials of Stem Cell Biology, edited by Robert Lana and Anthony Atala

BMS 609

Human Embryology and Developmental Biology Elective, Credits: 3 (3+0) Prereq: Biology of Human Disease The course covers the biology of fertilization and embryological development, with focus on the anatomical, cellular and molecular mechanisms that control human development. Topics include morphological changes associated with different stages of development, organogenesis, and their underlying molecular mechanisms, pathological malformations and congenital anomalies. Textbook: Developmental Biology Scott F. Gilbert and Michael J. F. Barresi

BMS 610

Translational Medical Sciences 3 Elective, Credits: 3 (3+0) Prereq: Biology of Human disease Translational medicine covers the expanding field of applying biomedical science research from the bench to the bedside. The course provides students with tools to develop laboratory research into medical therapeutics, develop diagnostics tools and implement treatment protocols. Text: Translational Medicine - The Future of Therapy


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BMS 611

Selected Topics in Regenerative Medicine Elective, Credits: 3 (3+0) Prereq: Stem Cell Biology and Regenerative Medicine This course covers current topics in stem cell research and regenerative medicine. New and current, as well as landmark papers on the field will be discussed in interactive learning setting. Major papers on topics such as cloning, embryonic stem cell lines, 3-D scaffolds, induced pluripotent stem will be discussed. Papers on new concepts, techniques and therapeutic discoveries in regenerative medicine will also be covered.

BMS 612

Advanced topics in Microbiology Elective, Credits: 3 (3+0) Prereq: This course will cover topics related to antibiotic resistance, diseases and biotechnology, bioterrorism, microbial biotechnology and archaeal, viral and prion biology. It will also help students to learn microbial biology and physiology. Text: Brock Biology of Microorganisms, 15 th Edition, Michael Madigan, Kelly Bender, Daniel Buckley, W. Sattley, David Stahl.

BMS 613

Microbiology of Human Pathogens Elective, Credits: 3 (3+0) Prereq: This course will help students to understand the clinically important aspects of microbiology and immunity and to address health problems related to microbial infections which exist in today’s life. The course topics will be discussed using problem-based learning and case studies. Text: Jawetz, Melnick, & Adelberg’s Medical Microbiology. Karen C. Carroll, Jeffery A. Hobden, Steve Miller, Stephen A. Morse, Timothy A. Mietzner, Barbara Detrick, Thomas G. Mitchell, James H. McKerrow, Judy A. Sakanari. Microbiology An Introduction, Gerard J. Tortora, Berdell R. Funke, and Christine L.

BMS 614

Bacteriophage Biology Elective, Credits: 3 (3+0) Prereq: This course is designed for students to be able to comprehend general concepts, methods, techniques, and applications of phage therapeutics in biomedical sciences. Lectures will focus on basic of phage, genomics of phage, phage ecology and phage therapy. This course will be applied through a student-based seminar where they will be able to discuss a topic. It is merely an interactive course where students are encouraged to participate in several scientific course–related activities to ensure full comprehension and application of phage use. Text: BACTERIOPHAGES Biology and Applications, Elizabeth Kutter and Alexander Sulakvelidze


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BMS 615

Cellular and Molecular Immunology (3 Cr) Elective, Credits: 3 (3+0) Prereq: Throughout the timeline of evolution, hosts and pathogens have co-evolved side by side. Pathogens developed new attacking mechanisms while hosts developed new defense strategies. Immunology is the study of the host-side immune system, including its responses to microbial pathogens and damaged tissues and its role in disease. In this course we will discuss in details both the innate and adaptive immune responses and how they interact together. The molecular aspects of various immune mechanisms will be discussed. Aberrant immune responses leading to some diseases like autoimmunity, immunodeficiency and hypersensitivity will be covered as well. The role of the immune system in organ transplantation and cancer will be addressed and special emphasis will be given to these translational aspects of immunology. Text: “Cellular and Molecular Immunology; 9 th edition, by Abbas

BMS 616

Molecular Virology and viral pathogenesis Elective, Credits: 3 (3+0) Prereq: The course covers several topics related to the virus molecular biology and the viral pathogenesis. Topics include virus attachment and entry, intracellular trafficking, assembly, exit and maturation, barriers to infection, host response, mechanisms of pathogenesis, cellular transformation and oncogenesis, vaccines, and antiviral drugs. Text: - Fundamental Neuroscience, 4th Edition Editors: Larry Squire Darwin Berg Floyd E. Bloom Sascha du Lac Anirvan Ghosh Nicholas C. Spitzer - Neurodegenerative Diseases: Clinical Aspects, Molecular Genetics and Biomarkers, 2nd ed.

BMS 617

Selected Topics in Molecular and cellular Biology Elective, Credits: 3 (3+0) Prereq: This course covers current topics in Molecular and cellular Biology. It will have both lecture and group discussion.

BMS 618

Methods in Molecular Biology Elective, Credits: 3 (3+0) Prereq: This course covers the theoretical basis to understand current and advanced techniques used in modern molecular biology research. Methods include NGS, proteomics, gene expression, qRT-PCR, primer design, site-directed mutagenesis, microarray, in situ hybridization, genetic engineering, protein interaction and localization. Some labs and bioinformatics tools may be included. Text: Several sources such as Springer protocols, Nature methods and Biotechniques in addition to kit manuals


81

BMS 619

DNA repair and Genome instability Elective, Credits: 3 (3+0) Prereq: The course describes the various mechanisms of repairing DNA damage by recombination. It also describes an overview of the four major pathways of homologous recombinational repair is followed by a description of the several mechanisms of nonhomologous end-joining. Text: -Genome Stability: DNA Repair and recombination, 2013, Haber - Genome stability: From virus to Human Application (Translational Epigenetics), 2016, Kovalchuk

BMS 620

Neurobiology of Disease Elective, Credits: 3 (3+0) Prereq: This course covers the molecular and cellular changes that are associated with many of neurodegenerative diseases. The course design to link basic science, disease-oriented research, and translational research. The reading material associated with the course will be primary research papers and there will be an emphasis on modern experimental techniques and research areas. Text: - Fundamental Neuroscience, 4th Edition Editors: Larry Squire Darwin Berg Floyd E. Bloom Sascha du Lac Anirvan Ghosh Nicholas C. Spitzer - Neurodegenerative Diseases: Clinical Aspects, Molecular Genetics and Biomarkers, 2nd

BMS 621

Advanced Cancer Biology Elective, Credits: 3 (3+0) Prereq: The course introduces a comprehensive overview on the advanced biology of cancer at the molecular and cellular level. It covers the nature of cancer cellular oncogenes, cytoplasmic signaling circuitry programs many of the traits of cancer, tumor suppressor gene, p53 and apoptosis: master guardian and executioner, eternal life: cell Immortalization and tumorigenesis, the rational Treatment of cancer, Crowd control: tumor immunology and immunotherapy” Text: - The Biology of Cancer by Robert Weinberg, ISBN 9780815342205, - Cancer Signaling: From Molecular Biology to Targeted Therapy 1st Edition by Christoph Wagener, Carol Stocking, Oliver Miller

BMS 622

Cancer Metabolism (3 Cr) Elective, Credits: 3 (3+0) Prereq: The course introduces a detailed overview on the metabolism related to cancer. It covers controversies related to the origins of cancer, its energy metabolism, metabolic solutions for its management and prevention. Text: Cancer as a Metabolic Disease: On the Origin, Management, and Prevention of Cancer By Thomas Seyfried.

BMS 623

Advanced Topics in Cancer Research Elective, Credits: 3 (3+0) Prereq: The course will cover advanced topics in cancer research. It will have both lecture and group discussion.


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BMS 624

Advanced human genome and disease Elective, Credits: 3 (3+0) Prereq: : This course will be divided into two sections. The first part includes discussing different types of genetic diseases and the second part discusses current and potential therapies for genetic diseases. The course is offered as an advanced version of the Human Genome and Disease course taught to the undergraduate students. Therefore, this course is tailored to the graduate students in the sense that it will focus on more advanced and detailed information regarding the genetic causes of diseases and the therapeutics. Moreover, for every topic discussed, a journal club will be held. Graduate students will be required to prepare presentations and present high impact publications that support the lectures. All graduate students should participate in open discussions to exchange information and debate over different hypotheses. Text: - Molecular Diagnostics for the clinical laboratorian ISBN 978-1- 58829-356- Moreover, part of the delivered material will be based on high-impact publications and reviews addressing the respective topics.

BMS 625

Genomic and proteomic techniques in research and diagnostics Elective, Credits: 3 (3+0) Prereq: This course will expose students to various advanced genomic and proteomic techniques used in research and molecular diagnostics. Students will learn how these techniques are used in the lab for research purpose and also their application in medical diagnostics. The techniques used for research purpose to be discussed include high-throughput drug library screening, yeast library screening, yeast two-hybrid, protein purification, fluorescence microscopy, super-resolution microscopy, electron microscopy, flow cytometry, ELISA, immunoblotting, chromatography and mass-spectrometry. Text: Principles and Techniques of Biochemistry and Molecular Biology Seventh edition.EDITED BY KEITH WILSON AND JOHN WALKER

BMS 626

Modern Synthetic Protocols Elective, Credits:3(3+0) Prereq: This class will cover modern synthetic routes utilized in drug synthesis. Topics will include retrosynthetic analysis of complex biologically active molecules, functional groups transformations, and important name reactions. Textbook: Advanced Organic Chemistry: Part B: Reaction and Synthesis, 5th Edition, By Francis A. Carey and Richard J. Sundberg, ISBN-13: 978-0387683546

BMS 691

Selected Topics in Chemical Biology Elective, Credits: 3 (3+0) This course discusses emerging topics in Chemical Biology

BMS 792

Selected Topics in Drug Design Elective, Credits: 3 (3+0) This course discusses emerging topics in Drug Design


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BMS 793

Selected Topics in Cancer Biology Elective, Credits: 3 (3+0) This course discusses emerging topics in Cancer Biology

BMS 794

Selected Topics in Stem cells & regenerative medicine Elective, Credits: 3 (3+0) This course discusses emerging topics in Stem cells & regenerative medicine

BMS 795

Selected Topics in medical genetics & genomics Elective, Credits: 3 (3+0) This course discusses emerging topics in Medical Genetics & Genomics

BMS 796 Selected Topics in Computational Biology Elective, Credits: 3 (3+0) This course discusses emerging topics in Computational Biology

BMS 797 Selected Topics in Micro Biology & immunology Elective, Credits: 3 (3+0) This course discusses emerging topics in Micro Biology & immunology

8.2.2. COURSE CATALOG FOR GRADUATE NANO-SCIENCE PROGRAM

NANOSC 601

Introduction to Nanoscience and Technology Compulsory, Credits: 3 (3+0) Prereq: The course introduces the students to the nanophenomenon from chemistry and physics perspectives. Besides, it covers the general development, characterizations and recent potential applications of a wide range of emerging nanomaterials including; graphene, fullerene, carbon nano-tubes, carbon nanofibers, polymeric nanoparticles, metallic, and metal oxide nanostructures. The course is taught in sessions including students’ presentations and group discussions of the book and recent research articles on the topics covered with formal written exams. Text:. Author: S.M. LINDSAY. 2010 Edition. Oxford University Press Inc., New York, USA. Nano: The Essentials Understanding Nanoscience and Nanotechnolgy. Author: T.PRADEEP. Tata McGraw-Hill Publishing Company Limited, NEW DELHI,


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NANOSC 602

Advanced Organic Chemistry Compulsory, Credits: 3 (3+0) Prereq: A one semester course that introduces students to the advanced organic chemistry concepts and molecular structures, structure and function of major classes of organic compounds. Organic reactions mechanisms. Advanced stereochemistry, ring systems, heterocyclic and alicyclic chemistry, and Aromatic compounds preparations and reactions. This course also covers advanced topics in organic synthesis like bond-making reactions, types of substitutions, reactivity trends in aromatic and aliphatic systems, functional groups and their transformations, free-radical reactions. The course will be taught as a series of lectures with formal written exams. Text: Advanced Organic Chemistry Part A: Structure and Mechanisms. Authors: Carey, Francis A., Sundberg, Richard J.

NANOSC 603

Advanced Polymer Chemistry Compulsory, Credits: 3 (3+0) Prereq: This course covers the different polymerization techniques, characterization, and analysis of their physical properties. Besides, it covers the statistical mechanics and thermodynamics of polymers in various environments, gels, and hydrogel development. The course is taught in sessions including students’ presentations and group discussions of the book and recent research articles on the topics covered with formal written exams. Text: Principles of Polymer Chemistry, 3rd Edition. Author: A. Ravve. ISBN 978-1- 4614-2211- 2 ISBN 978-1- 4614-2212- 9 (eBook), DOI 10.1007/978-1- 4614-2212- 9. Springer New York Heidelberg Dordrecht London, 2012.

NANOSC 604

Advanced Characterization Techniques Compulsory, Credits: 3 (3+0) Prereq: This course aims at introducing students to a myriad of characterization techniques indispensable to scientists and researchers working in the area of materials science and the nanoscale science in general. The course includes detailed discussion of the underlying principles as well as practical notes on selected characterization techniques including several microscopic, spectroscopic, as well as surface characterization techniques. The course will be taught as a series of lectures with formal written exams, several practical sessions will be conducted as well to demonstrate the principles and applications of certain techniques fully. Text: (1) Surface Analysis Methods in Materials Science (Editors: O'Connor, John, Sexton, Brett, Smart,Roger S.C. (Eds.) (2) Quantitative Chemical Analysis. Daniel C. Harris (Michelson Laboratory, China Lake).


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NANOSC 605

Advanced Molecular Spectroscopy Elective, Credits: 3 (3+0) Prereq: A one semester course that covers electromagnetic spectrum and radiation-matter interaction. Different regions of the electromagnetic spectrum and associated nuclear, electronic, and molecular spectroscopy will be studied. Besides, the students will be introduced to spectrum acquisition and processing with emphasis on the spectroscopy of nanomaterials. Text: (1) Fundamentals of Molecular & Spectroscopy. Author, Banwell. Edition, reprint. Publisher, McGrawHill Education (India) Pvt Limited, 1994. (2) Atomic and Molecular Spectroscopy: Basic Concepts and Applications. 1st Edition. Author: Rita Kakkar. Cambridge.

NANOSC 606

Transition metals and main group chemistry Elective, Credits: 3 (3+0) Prereq: This course aims at consolidating student’s knowledge in inorganic chemistry including main group metals and chemistry of transition metals. Topics covered include the different models of binding and hybridization of transition metals, transition metal complexes, nomenclature, trends in activity and physicochemical properties, types of reactions and substitutions on transition metal centers, transition metals in catalysis, as well as description of certain analytical techniques commonly utilized in research conducted on transition metal complexes. The course will be taught as a series of lectures with formal written exams. Text: Inorganic Chemistry: Principles of Structure and Reactivity. James E. Huheey, Ellen A. Keiter, Richard L. Keiter, Okhil K. Medhi. Prentice Hall; 4 edition

NANOSC 607

Nanomedicine Elective, Credits: 3 (3+0) Prereq: A one semester course that introduces the students to the main medical applications of nanoscience & technology. Particularly in (i) drug delivery applications, (ii) diagnosis, and (iii) tissue engineering applications. The course is taught in sessions including students’ presentations and group discussions of the book and recent research articles on the topics covered with formal written exams. Text: Understanding Nanomedicine: An Introductory Textbook, Rob Burgess, 2012 by Taylor & Francis Group, LLC

NANOSC 608/708

Nanomaterials for Biomedical Applications Elective, Credits: 3 (3+0) Prereq: This course focuses particularly on bottom-up synthesis of nanostructures (such as; vai self-organization and molecular self-assembly), and then on the biomedical applications of the developed nanostructures. The course is taught in sessions including students’ presentations and group discussions of the book and recent research articles on the topics covered with formal written exams. Text: Nanomaterials for Medical Diagnosis and Therapy. Edited by Challa S. S. R. Kumar. Copyright 8 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


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NANOSC 609/709

Advanced Pulmonary Drug Delivery Elective, Credits: 3 (3+0) Prereq: This course focuses particularly on the advanced approaches for development of carriers for controlled pulmonary drug delivery. The course is taught in sessions including students’ presentations and group discussions of the book and recent research articles on the topics covered with formal written exams. Text: Updates on polymers for pulmonary drug delivery. Editors: Smyth H., El-Sherbiny, I. M., & McConvel J., Smithers Rapra, Akron, OH, USA (2013).

NANOSC 610/710

Macromolecules Elective, Credits: 3 (3+0) Prereq: In this course the students will get introduced to the main concepts of Macromolecules. This course aims at providing detailed introduction to specific class of materials of paramount importance in current daily life. Essential knowledge and understanding of the different nature, structure, and properties of macromolecules will be covered. In addition, several pathways to construct and characterize macromolecules will be discussed. Finally, quantitative methods used in characterization of macromolecules will be discussed. The course is taught in sessions including students’ presentations and group discussions of the book and recent research articles on the topics covered with formal written exams. Text: Macromolecules: An Introduction to Macromolecules. Authors: Leo Mandelkern.

NANOSC 611/711

Catalysis Elective, Credits: 3 (3+0) Prereq: In this course many of industrially-relevant catalytic processes will be discussed. Discussion of several models of chemical kinetics and the criteria for catalyst design will be visited. Additionally, principles and examples of applications on surface-catalysis and heterogeneous catalysis will be demonstrated. Catalyst supports, shaping and testing will also be discussed in this course that will be taught as a series of lectures with formal written exams. Text: (1) Concepts of modern catalysis and kinetics, 2nd edition, Chorkendorff & Niemantsverdriet, Wiley- VCH. (2) Inorganic Chemistry: Principles of Structure and Reactivity (4th Edition). James E. Huheey

NANOSC 612/712

Self-assembly Elective, Credits: 3 (3+0) Prereq: Introduction to molecular recognition, complimentary chemical functionalities, reversible interactions and self-assembly. The course will also cover supramolecular chemistry, crystal engineering, cocrystals, and polymorphism. The course is taught in interactive manner through regular sessions with strong emphasis on group discussions and students presentations with formal written exams. Text: Self-assembly in supramolecular systems, Ian M Atkinson, Len F Lindoy, The Royal Society of Chemistry 2000. Inorganic Chemistry: Principles of Structure and Reactivity (4th Edition). James E. Huheey,


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NANOSC 713

Selected topics in Nano-scale Sciences Elective, Credits: 3 (3+0) This course discusses emerging topics in Nano-scale Scienes

8.2.3. COURSE CATALOG FOR GRADUATE PHYSICS PROGRAM

PHY 601 Quantum Mechanics I Compulsory, Credits: 3 (3+0) Prereq: One semester course in quantum mechanics. The topics covered are: fundamental concepts of quantum mechanics, quantum dynamics, theory of angular momentum, symmetry in quantum mechanics. Text: (1) J. J. Sakurai and Jim Napolitano, Modern Quantum Mechanics. (2) R. Shankar, Principles of Quantum Mechanics. (3) B. R. Desai, Quantum Mechanics with Basic Field Theory. (4) B. H. Branden and C. J. Joachain, Quantum Mechanics. (5) N. Zettili, Quantum Mechanics: Concepts and Applications. (6) C. Tannoudji et al, Quantum Mechanics I, II

PHY 602 Quantum Mechanics II Compulsory, Credits: 3 (3+0) Prereq: PHY 601 One semester course in quantum mechanics. The topics covered are approximation methods, scattering theory, identical particles, quantum theory of radiation, relativistic quantum mechanics. Text: (1) J. J. Sakurai and Jim Napolitano, Modern Quantum Mechanics. (2) R. Shankar, Principles of Quantum Mechanics. (3) B. R. Desai, Quantum Mechanics with Basic Field Theory. (4) B. H. Branden and C. J. Joachain, Quantum Mechanics. (5) N. Zettili, Quantum Mechanics: Concepts and Applications. (6) C. Tannoudji et al, Quantum Mechanics I, II

PHY 603 Electrodynamics I Compulsory, Credits: 3 (3+0) Prereq: One semester course in electrodynamics. The topics covered are: Basic principles of electromagnetism: electrostatics, magneto statics, Electromagnetic induction. Text: (1) J. D. Jackson Classical Electrodynamics. (2) Esward Purcell, Electricity and Magnetism. (3) Richard Feynman, Lectures on Physics, Vol. 2.


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PHY 604 Electrodynamics II Elective, Credits: 3 (3+0) Prereq: PHY 603 One semester course in electrodynamics. The topics covered are: Electric and Magnetic Properties of Matter, conservation laws in electrodynamics, relativistic Electrodynamics. Text: (1) J. D. Jackson Classical Electrodynamics. (2) Esward Purcell, Electricity and Magnetism. (3) Richard Feynman, Lectures on Physics, Vol. 2.

PHY 605 Statistical Mechanics I Elective, Credits: 3 (3+0) Prereq: One semester course in statistical mechanics. The topics covered are: the laws of thermodynamics and the concept of temperature, work, heat and entropy. Postulates of classical statistical mechanics, micro canonical, canonical and grand canonical distributions, applications to vibrations, ideal gas and photon gas. Text: (1) David Chandler Introduction to Modern Statistical Mechanics. (2) James Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity. (3) Mehran Kerdar, Statistical Physics of Particles. (4) Mehran Kerdar, Statistical Physics of Fields. (5) R. K. Pathria and Paul D. Beale Statistical Mechanics.

PHY 606 Statistical Mechanics II Elective, Credits: 3 (3+0) Prereq: PHY 605 One semester course in statistical mechanics. The topics covered are: Quantum statistical mechanics, Fermi and Bose systems. Interacting systems: cluster expansion, van der Waal’s gas, and mean field theory. Topics form modern statistical mechanics are explored. The hydrodynamic limit and classical field theories. Phase transitions and broken symmetries: universality, correlation functions, and scaling theory. The renormalization approach to collective phenomena. Dynamic critical behavior. Random systems. Text: Text: (1) David Chandler Introduction to Modern Statistical Mechanics. (2) James Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity. (3) Mehran Kerdar, Statistical Physics of Particles. (4) Mehran Kerdar, Statistical Physics of Fields. (5) R. K. Pathria and Paul D. Beale Statistical Mechanics.

PHY 607 Classical Mechanics Compulsory, Credits: 3 (3+0) Prereq: One semester course in classical mechanics. The topics covered are: Variational principles and Lagrange’s equations, central force problem, kinematics and dynamics of rigid bodies, small oscillations, Hamilton equation of motion, canonical transformations, Hamilton-Jacobi theory, and classical theory of fields. Text: (1) Herbert Goldstein, Classical Mechanics. (2) L. D. Landau and E. M. Lifshitz, Mechanics. (3) L. D. Landau and E. M. Lifshitz, Classical Theory of Fields.


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PHY 608 Mathematical Physics I Elective, Credits: 3 (3+0) Prereq: One semester course in mathematical physics. The topics covered are: Series solution of differential equations, Sturm-Lowville theory, Green’s functions, and complex variables. Text: (1) G. Arfken H. J. Weber, F. E. Harris, Mathematical Methods for Physicists: A Comprehensive Guide. (2) G. F. Carrier, M. Krook, C. E. Pearson, Functions of a Complex Variable: Theory and Technique. (3) Hassani, Sadri, Mathematical Physics: A Modern Introduction to Its Foundation

PHY 611 Quantum Field Theory1 Elective, Credits 3 (3+0) Prereq: PHY 601 Photons and the electromagnetic field, Lagrangian field theory and Klein-Gordon field, the Dirac field, covariant quantization of the electromagnetic field, the S-matrix expansion, Feynman diagrams and rules in QED; QED processes in lowest order, radiative corrections, regularization and renormalization. Recommended textbooks 1) Quantum Field Theory, Franz Mandl, Graham Shaw. 2) Quantum Field Theory, Lewis H. Ryder. 3) Daniel V. Schroeder and Michael Peskin, An Introduction to Quantum Field Theory

PHY 617 Chemical Biology Elective, Credits 3 (3+0) Prereq: This course will provide a high-level overview on the structure, function and chemistry of biological macromolecules including proteins, nucleic acids and carbohydrates. Topics include protein and nucleic acid folding, energetics of macromolecular interactions (kinetics and thermodynamics) and mechanistic enzymology. Using specific examples from the current literature, each topic will stress how chemists have used molecular level tools and probes to help understand the specific systems under study. The overarching theme in this course will be that structure and function are intimately linked. Texts: Essentials of Chemical Biology: Structure and Dynamics of Biological Macromolecules, Andrew Miller and Julian Tanner, John Wiley & Sons, 2008, ISBN 978-0-470-84531-8 and related literature International model: https://www.aem.umn.edu/~shield/csecc/minutes/2014-4-22/chem4423w-syllabus.pdf and http://webprod3.leeds.ac.uk/catalogue/dynmodules.asp?Y=201718&F=P&M=CHEM-3010 and https://mcb.berkeley.edu/courses/syllabi/mcbc212_sp2014.pdf

https://www.aem.umn.edu/~shield/csecc/minutes/2014-4-22/chem4423w-syllabus.pdf

https://www.aem.umn.edu/~shield/csecc/minutes/2014-4-22/chem4423w-syllabus.pdf

http://webprod3.leeds.ac.uk/catalogue/dynmodules.asp?Y=201718&F=P&M=CHEM-3010

http://webprod3.leeds.ac.uk/catalogue/dynmodules.asp?Y=201718&F=P&M=CHEM-3010

https://mcb.berkeley.edu/courses/syllabi/mcbc212_sp2014.pdf


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PHY 618

Cellular Biochemistry Elective, Credits 3 (3+0) Prereq: This course will cover the synthesis, structure, and function of macromolecules. Students will learn the biochemical pathways and their regulations within prokaryotic and eukaryotic cells. Cell communications will be covered including cell signalling and apoptosis. The topics will be covered by both textbooks and current literature.

PHY 619 Applied molecular modelling Elective, Credits 3 (3+0) Prereq: Description: This course aims to consolidate students’ knowledge and skills in the fields of Molecular models; Molecular Dynamics and Monte Carlo approaches, visualization. This will be achieved through a set of seminars and hands-on experience in designing, customizing and use of programs to study small molecules and biomolecules. The course will introduce students to simulation techniques for their research. Principal textbook: D. Frenkel and B. Smit, Understanding molecular simulation. From algorithms to applications, Academic 2002. Supplementary text: A. R. Leach, Molecular modelling. Principles and applications, Pearson Education, 2009 (Virginia Commonwealth University) and https://www.mccormick.northwestern.edu/chemical-biological/courses/descriptions/451-0.html

PHY 620 Optics and Spectroscopy Elective, Credit 3 (3+0) Description: This course covers principles underlying electromagnetic radiation with applications to lasers, spectroscopy and imaging in mind. Topics include: Maxwell’s equations, wave mechanics, the wave equation, blackbody radiation, the electromagnetic spectrum, lasers, absorption and refractive index, superposition, standing waves and group velocity, Fresnel’s equations, polarization and optical activity, geometric optics, Fourier Series and the Fourier Transform, convolution and correlation, interferometry, light scattering, diffraction, diffraction gratings and lenses, small beams and short pulses. Principal textbook: Optics (5th Edition) 5th Edition by Eugene Hecht Pearson; 5 edition, ISBN-13: 978-0133977226, ISBN-10: 0133977226 Supplementary texts: J. F. James, A Student’s Guide to Fourier Transforms: with applications to physics and engineering, Third Edition, Cambridge

PHY 712 Quantum Field Theory 2 Elective, Credits 3 (3+0) Prereq: Weak interactions, gauge theory of weak interactions, spontaneous symmetry breaking, electroweak theory and the standard model. Recommended textbooks 1) Quantum Field Theory, Franz Mandl, Graham Shaw. 2) Quantum Field Theory, Lewis H. Ryder. 3) Daniel V. Schroeder and Michael Peskin, An Introduction to Quantum Field Theory

https://www.mccormick.northwestern.edu/chemical-biological/courses/descriptions/451-0.html

https://www.mccormick.northwestern.edu/chemical-biological/courses/descriptions/451-0.html

https://www.amazon.com/Eugene-Hecht/e/B000APJKQ0/ref=dp_byline_cont_book_1


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PHY 713 General Relativity and Cosmology Elective, Credits 3 (3+0) Prereq: Special relativity and flat spacetime, manifolds, curvature, gravitation, weak fiends and gravitational radiation, Schwarzschild solution and black holes, cosmology. Recommended textbook 1) Spacetime and Geometry: An Introduction to General Relativity, Sean Carroll

PHY 714 Supersymmetry Elective, Credits 3 (3+0) Prereq: Physical motivation, Supersymmetry algebra and representations, superspace and superfields, Four-dimensional supersymmetric Lagrangians, Supersymmetry breaking, non-renormalization theorems, introductions to MSSM. Recommended textbooks 1) Supersymmetry and Supergravity, Wess and Bagger 2) Supersymmetric gauge field theory and string theory, Bailin and Love 3) Supersymmetry in particle physics, Aithchison 4) Journeys Beyond The Standard Model, P. Ramond

PHY 715 Standard Model Elective, Credits 3 (3+0) Prereq: Difficulties of the pre-gauge theory, global and local gauge invariance, spontaneous symmetry breaking, Goldstone bosons and the Higgs mechanism, The Standard Model of electroweak interactions, Electroweak phenomenology Flavour dynamics, Electromagnetic interactions of leptons and hadrons, An introduction to Quantum Chromodynamics (QCD), Phenomenology of the Standard Model, Neutrino physics. Recommended textbooks 1) Quantum Field Theory, Franz Mandl, Graham Shaw. 3) Daniel V. Schroeder and Michael Peskin, An Introduction to Quantum Field Theory 4) Introduction to Elementary Particles, D. Griffiths 5) Gauge Theory of Elementary Particle Physics, Cheng and L. Li 6) Introduction to High Energy Physics, Perkins.

PHY 716 Group Theory and Lie Albebra Elective, Credits 3 (3+0) Prereq: Symmetry in classical and quantum physics, introduction to group theory, representations of groups, discrete groups, direct products, symmetric groups, continuous groups, Lie groups, SU(n) groups, Lorentz group, Lie algebras, representations of Lie algebras, Cartan Classification. Recommended textbooks 1) Lie algebras in particle physics, Howard Georgi. 2) Physics from symmetry, Jakob Schwichtenberg.


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PHY 721 Computational Physics Elective, Credits 3 (3+0) Description: This course covers the computational methods of special interest in physics, biophysics, astronomy and atmospheric science. Topics include numerical differentiation, advection-diffusion phenomena, wave equation, eigenvalue problem with application to quantum systems, thermodynamic systems, molecular mechanics. Supplementary advanced topics will be covered for postgraduate students according to student and instructor interest. Principal textbook: Computational Physics: Simulation of Classical and Quantum Systems, Philipp Scherrer, Second Edition. ISBN 978-3-319-00401-3.

PHY 722 Biological Physics Elective, Credits 3 (3+0) Prereq: Description: The course emphasizes molecular biophysics, but requires a stronger mathematical and physical background than the undergraduate course offered in BMS. The first half of the course covers structure, dynamics and molecular interactions of proteins and nucleic acids and small molecule drugs and metabolites. Common structural motifs are linked to underlying physical principles governing bimolecular folding and dynamics and to the biological function, which is presented as a brief overview for students from a physical background. The second half of the course is dedicated to physical techniques for studying these systems. Nuclear magnetic resonance, X-ray crystallography, Cryo-electron microscopy, fluorescence and other spectroscopic methods (in bulk and as single molecules), surface plasmon resonance, and other methods may be treated explicitly according to student and instructor interest. Principal textbook? The only textbook which covers the material at a Physics postgraduate level is old: Biophysical Chemistry: Part III The Behavior of Biological Macromolecules, by Charles Cantor and Paul Schimmel. The text will therefore be supplemented by original literature and undergraduate level texts Supplementary texts: Nelson, Philip. Biological Physics: Energy, Information, Life. New York, NY: W.H. Freeman, c2003. ISBN: 9780716743729 and Principles of Physical Biochemistry, Kensal E van Holde, Curtis Johnson, Pui Shing Ho, 2006 |Pearson Second Edition

PHY 723 Selected topics in High Energy Physics Elective, Credits: 3 (3+0) This course discusses emerging topics in High Energy Physics

PHY 724 Selected topics in Astrophysics Elective, Credits: 3 (3+0) This course discusses emerging topics in Astrophysics

PHY 725 Selected topics in Computational Physics Elective, Credits: 3 (3+0) This course discusses emerging topics in computational physics


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8.2.4. COURSE CATALOG FOR GRADUATE MATH PROGRAM

MATH 601

Advanced Computational Methods (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: This course aims at introducing the fundamentals of modern numerical techniques of linear algebra. Topics include sparse/dense matrices, QR decomposition, LU decomposition, Cholesky decomposition, power method, Householder reflection, eigenvalue problem, SVD, Krylov subspaces. It also discusses advanced numerical methods for solving wide range of linear and nonlinear elliptic, parabolic and hyperbolic partial differential equations and integral equations central to a wide variety of applications in science and engineering fields. Topics include finite differences, finite volume, finite elements, integral equations, and the boundary element method. Recommended Textbooks: 1. David S. Watkins, Fundamentals of Matrix Computations 3rd Edition, Wiley,

2010. 2. Lloyd N. Trefethen and David Bau, Numerical Linear Algebra, SIAM, 1997. 3. J.W. Thomas, Introduction to Numerical Methods for Partial Differential

Equations, Springer, 1995.

MATH 602 Introduction to Optimization (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: The course gives an introduction to linear/nonlinear optimization formulation of engineering and scientific problems and learns fundamental algorithms and general duality concepts of continuous constrained/unconstrained optimization. The course also explores the optimality conditions, duality, sensitivity analysis and algorithms. A brief account of convex optimization and integer programming are also discussed. Recommended Textbooks: 1. Singiresu S. Rao, Engineering Optimization: Theory and Practice, 4th edition,

John Wiley & Sons, 2009 2. Jorge Nocedal and S.J. Wright, Numerical Optimization, 2nd edition, Springer,

2006. 3. David Luenberger and Yinyu Ye, Linear and Nonlinear Programming, 4th edition,

Springer, 2016

MATH 603 Real Analysis (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: The aim of this course is to provide a very solid foundation for real analysis to build differential calculus from the axioms of the real numbers with a strong emphasis on formality. Topics covered include: definitions and properties of real numbers, sequences, upper and lower limits, series, differentiable functions, equicontinuous families, topological properties of the real line, open and closed sets in the real numbers, compact sets, Heine-Borel theorem and continuous functions, inverse and implicit function theorems and rank theorem. Recommended Textbooks: 1. Robert G. Bartle, Donald R. Sherbert, Introduction to Real Analysis, 4th edition,

New York: John Wiley and Sons, 2011. 2. William R. Wade, An introduction to analysis, Pearson Prentice Hall, 2004.


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MATH 604 Advanced Complex Analysis (3 Cr) Elective, Credits: 3 (3+0) Prereq: This course introduces the theory of functions of a complex variable and their differentiation and integration in the complex plane. It provides a comprehensive introduction to complex variable theory and its applications to current engineering problems. Topics include complex numbers and the complex plane, analytic functions, Cauchy-Riemann equations, harmonic functions, elementary functions, multiple-valued functions, integration in the complex plane, Taylor and Laurent series representation of analytic functions, and the residue theorem for the contour integrals and its applications to evaluate definite integrals and the inverse Laplace transform. In addition, it covers the conformal mapping and the linear fractional transformations and its applications in solving some boundary-value PDE problems in the areas of fluid flow, heat flow, gravitation, and electrostatics. The course discusses also the properties of analytic mappings, analytic approximation and continuation, harmonic and subharmonic functions and basics of holomorphic functions. Recommended Textbook: 1. Edward Saff and Arthur D. Snider, Fundamentals of Complex Analysis: with

Applications to Engineering and Science, 3rd ed., Pearson, 2013. 2. Joseph Bak and Donald J. Newman, Complex Analysis, 3rd ed. ,Springer, 2010.

MATH 605 Regression Analysis (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: This course offers an introduction to modern methods of regression analysis with applications. The overall goal of the course meeting the demand is to acquaint students with the statistical methodology of the regression modelling and to develop advanced practical skills that are necessary for applying regression techniques to a real-world data analysis problem. The statistical principles and techniques are established and used to select a suitable model for a given dataset along with detailed implementation of the models within real data examples using the statistical software MINITAB and R. Recommended Textbooks: 1. M. Kutner, C. Nachtsheim, J. Neter , W. Li, Applied linear statistical models" ,5th

edition, 2004. 2. D. C. Montgomery, E. A. Peck, Introduction to Linear Regression Analysis, Wiley,

2011.

MATH 606

Convex Optimization (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: Math 602 (Introduction to Optimization) The course focuses on recognizing and solving convex optimization problems that arise in applications. It includes convex sets, functions, basics of convex analysis, least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, optimality conditions, duality theory, theorems of alternative, and applications. Recommended Textbooks: 1. Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge University Press, 2004. 2. Dimitri Bertsekas, Convex Optimization Algorithms 1st edition, Athena Scientific, 2015.


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Math 607 Integer and Combinatorial Optimization (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: Math 602 (Introduction to Optimization) The course introduces integer programming and combinatorial optimization, emphasizing geometry, duality and algorithms. Topics include formulating problems in integer variables, enhancement of formulations, ideal formulations, integer programming duality, linear and semidefinite relaxations, cutting plane methods, Branch and bound, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization and mixed integer programming. Recommended Textbooks: 1. Dimitris Bertsimas, Robert Weismantel, Optimization over Integers, Dynamic Ideas, 2005. 2. George L. Nemhauser and Laurence A. Wolsey, Integer and Combinatorial Optimization, Wiley, 1988. 3. Jon Lee, A First Course in Combinatorial Optimization, Cambridge Texts in Applied Mathematics, 2004.

Math 608 Operations Research (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: Operations research (OR) has many applications in science, engineering, economics, and industry and thus the ability to solve OR problems is crucial for both researchers and practitioners. The course aims at formulating, analyzing, and solving mathematical models that represent real-world problems such as scheduling flights, routing mobile phone calls, managing investments and minimising risks. In particular, topics include linear programming, network flow problems, integer programs, nonlinear programs, dynamic programming and queueing models. Recommended Textbooks: 1. Hamdy Taha, Operations Research: An Introduction, 10th ed., Pearson, Prentice Hall, 2016. 2. F. S. Hillier and G. Lieberman, Introduction to Operations Research, McGraw-Hill Higher Education, 9th edition, 2009.

MATH 609

Stochastic Processes (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: The aim of this course is to introduce students to fundamentals and applications of stochastic processes. Topics include: Discrete-time Markov chains: Modelling of real life systems as Markov chains, transient behavior, limiting behavior and classification of states, Markov chains with costs and rewards, homogeneous and nonhomogeneous Poisson processes, Continuous-time Markov chains, birth-and-death processes in continuous time, basic queueing models, Brownian motion, Gaussian, Ornstein-Uhlenbeck and Lévy processes, Itô-calculus, approaches for stochastic simulation of random variables. Recommended Textbooks: 1. H. M Taylor and S. Karlin, An Introduction to Stochastic Modelling, Academic

Press, 4th edition, 1998. 2. S. Ross, Introduction to Probability Models, 7th edition, Academic Press, 2000. 3. Kurt Jacobs, Stochastic Processes for Physicists: Understanding Noisy Systems,

1st edition, Cambridge, 2010.


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MATH 610

Time Series Analysis (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 605 (Regression Analysis) Time series occur in a wide range of disciplines, ranging from business, economic and social sciences to engineering, applied science and biomedical contexts. In this course, more advanced statistical techniques available for these tasks (eg. Box-Jenkins ARMA/ARIMA-models) are discussed and underlying principles are explained. Furthermore, attention is paid to the analysis of multivariate time series that are cross-correlated (Transfer function models and XARIMA models). The use of the representative statistical software MINITAB and R, are demonstrated and participants get the opportunity for hands-on experience in analyzing and forecasting time series. Recommended Textbooks: 1. P. J. Brockwell and R.A. Davis, Introduction to Time Series and Forecasting, 3rd

ed., Springer, 2016. 2. D. C. Montgomery and C. L. Jennings, Time Series Analysis and Forecasting, 2nd

edition, Wiley, 2015.

MATH 611

Design of Experiments (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 605 (Regression Analysis) This course is planned for those interested in the design, conduct, and analysis of experiments in the physical, chemical, biological, medical, social, psychological, economic, engineering, or industrial sciences. The course will examine how to design experiments, carry them out, and analyze the data they yield. Various designs are discussed, and their respective differences, advantages, and disadvantages are noted. Experiments considered in the course include one factor experiments, randomized blocks, Latin squares, 2k-factorial experiments, and split plot experiments. The theory presented is exemplified with a large number of applications. Recommended Textbooks: 1. D.C. Montgomery, Design and Analysis of Experiments, 8th edition, 2012. 2. G.W. Oehlert, A First Course in Design and Analysis of Experiments, 2010.

MATH 612

Functional Analysis (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: Math 603 (Real Analysis) This course aims to introduce the basic concepts, principles and methods of Functional analysis and its applications. Topics include: metric, Banach and Hilbert spaces, Hahn-Banach and separation theorems, Bounded linear operators, Open mapping theorem, Closed graph theorem, Fredholm theory, Spectral theory of linear operators in normed spaces, Compact linear operators on normed spaces and their spectrum, Spectral theorem for bounded self-adjoint operators. In addition, topics of nonlinear operators are covered such as: Fréchet and Gâteaux derivatives and Liapunov-Schmidt method. Recommended Textbooks: 1. W. Rudin, Functional Analysis, 2nd edition, McGraw-Hill Inc., 1991. 2. Leonid P. Lebedev, Iosif I. Vorovich, Michael J. Cloud, Functional analysis in

mechanics, 2nd ed., Springer, 2013.


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MATH 613

Topology and Measure Theory (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: Math 603 (Real Analysis) Topology and measure theory provide a rigorous foundation for many of the advanced topics in mathematics. It begins with the concept of topological space, open and closed sets. Next how to classify objects based on the interior, exterior, boundary, limit points and closure of a set. Moreover, the product topology, connectedness and compactness are handled. Measure theory is core of modern probability theory and stochastic process. Topics in measure theory include: Algebras and Sigma-algebra, Carathéodory’s theorem, Measurable functions, Lebesgue integration, product measures and Fubini's theorem, Lp classes. Recommended Textbooks: 1. Stefan Waldmann, Topology: An Introduction, Springer, 2014. 2. L. F. Richardson, Measure and Integration: A Concise Introduction to Real

Analysis, John Wiley & Sons, Inc., 2009

MATH 614

Fractional Calculus (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: Historical origins of fractional calculus; Fractional integral according to Riemann-Liouville; Caputo fractional derivative; Riesz-Feller fractional derivative; Grünwal-Letnikov; Integral equations; Relaxation and oscillation equations; Fractional diffusion equation; A nonlinear fractional differential equation; Stochastic solution; Geometrical interpretation of fractional integration. Recommended Textbooks: 1. R. Herrmann, Fractional Calculus: An introduction for Physicists, 2nd edition,

Word Scientific Publishing Co., 2014. 2. S. G. Samko, A. A. Kilbas and O. I. Marichev, Fractional Integrals and Derivatives:

Theory and Applications, Gordon and Breach Science Publishers, 1993.

MATH 615

Machine Learning and Data Mining (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: The aim of this course is to introduce students to the concept of data mining and data-driven learning. Topics covered include: Data preparation, Visualization, feature extraction/selection (LDA, PCA, ICA), Classification (k-NN, Naïve Bayesian, Decision Trees, SVM), Clustering (k-means, GMM, Hierarchical), Regression (Linear, Ridge, Logistic), Model Assessment and Performance Evaluation. Recommended Textbooks: i. Stephen Marsland, Machine Learning: An Algorithmic Perspective, 2nd Edition,

2015. ii. Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques,

Third Edition (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition, 2011.

MATH 616

Big Data Analytics and Cloud Computing (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: The aim of this course is to introduce students to the Big Data Visualization, Processing and Analysis and its applications especially in context of Cloud. Topics covered include: NoSQL databases, Hadoop platform, Pig Latin and Hive, Spark, cloud-based big data analysis, analysis of problem space and data needs. Recommended Textbooks: 1. Govindaraju, Raghavan, and Rao, Big Data Analytics, 1st Edition, 2015 2. Simon Walkowiak, Big Data Analytics with R, 2016


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MATH 617

Artificial Neural Networks and Deep Learning (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: The aim of this course is to introduce students to the different artificial neural networks architectures such as: Feedforward Neural Networks (FNN), Radial Basis Function (RBF), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Autoencoders, Long Short-Term Memory (LSTM), the type of data each is used for and the learning algorithms for each architecture. Recommended Textbooks: 1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press,

2016. 2. Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow:

Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media; 1st edition, 2017.

MATH 711

Network Analysis and Advanced Linear Programming (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 602 (Introduction to Optimization) This course considers the concepts of advanced linear programming and network flows. It includes the theory of the simplex method, the revised simplex algorithm using LU factorization, and simplex for bounded variables and primal-dual methods; methods for solving large-scale models such as Danzig-Wolfe decomposition, Bender’s partitioning, Lagrangian relaxation, and subgradient optimization; computational complexity and Karmarkar’s algorithm; minimum cost network flows, network simplex, and generalized and multicommodity network flow problems; and special types of network problems including the traveling salesman, routing, network location, and reliability problems. Recommended Textbooks: 1. M. S. Bazaraa, J. Jarvis, H. D. Sherali, Linear Programming and Network Flows,

4th Edition, Wiley, 2010. 2. F. S. Hillier and G. J. Lieberman, Introduction to Operations Research, 10th

Edition., McGraw Hill, 2015.

MATH 712

Dynamic Optimization and Optimal Control (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 602 (Introduction to Optimization) The course aims at introducing the theory of sequential stochastic and deterministic optimization and its applications. A unified approach to optimal control of stochastic dynamic systems and Markovian decision problems is discussed. Optimal decision making under perfect and imperfect state information, certainty equivalent, open loop-feedback control, rollout, and other suboptimal control methods are introduced with various applications. Recommended Textbooks: 1. Dimitri Bertsekas, Dynamic Programming and Optimal Control, Vol. I, 4th

Edition, Athena Scientific, 2017. 2. John T. Betts, Practical Methods for Optimal Control and Estimation Using

Nonlinear Programming, 2nd ed., Advances in Design and Control, SIAM, 2009.


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MATH 713

Multiple Criteria Optimization (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 602 (Introduction to Optimization) The purpose of this course, is an introduction with the concepts, tools and techniques of decision making under multiple criteria. It covers: Utility functions, Nondominated Criterion Vectors, and Efficient Points; Point Estimate Weighted-Sums Approach; Classification of Multiobjective Programming Methods; Techniques for Generating Noninferior Solutions; Vectors Maximum Algorithms; Filtering and Set Discretization; Goal Programming; Multiple Objective Linear Fractional Programming; Interactive procedures; Interactive Weighted Tchebycheff Procedures. Recommended Textbooks: 1. R. E. Steuer, Multiple Criteria Optimization: Theory, Computation, and

Application, Wiley, 1986. 2. J. L. Cohon, Multiobjective Programming and Planning. Academic Press, 1978.

MATH 714

Number Theory and Cryptography (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: CSCI 101 (Introduction to Computer Science) This course introduces the theory of the elementary number theory with an emphasis on applications to cryptography. It introduces the study of patterns and relationships satisfied by natural numbers. Topics include divisibility, modular arithmetic, prime numbers, congruences, primitive roots, Fibonacci numbers, Euclid’s algorithm, modular arithmetic, multiplicative inverses, continued fractions and quadratic residues. The course covers computer and network applications using the cryptographic protocols for assuring the quality, validity and privacy of information using secret key, public key infrastructure and hash functions. Recommended Textbooks: 1. Neal Koblitz, A Course in Elementary Number Theory and Cryptography, 2nd

ed., Springer, 1994. 2. James Kraft, Lawrence Washington, An Introduction to Number Theory with

Cryptography (Textbooks in Mathematics) 2nd ed. Chapman and Hall, 2018. 3. Richard A. Mollin, Advanced Number Theory with Applications, CRC Press,

Taylor & Francis, 2017.

MATH 715

Advanced Functional Analysis (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 603, MATH 612, MATH 613 This course will focus on more advanced topics in functional analysis. Subjects may include operator theory on Hilbert space; operators on Banach spaces; locally convex spaces; Sobolev spaces; vector measures; Banach algebras; C*-Algebras; test function spaces and tempered distributions; semi-groups of operators and applications to PDE; Fourier transforms and applications to differential and integral equations. Recommended Textbooks: 1. R. G. Douglas, Banach Algebra Techniques in Operator Theory, 2nd Edition,

Graduate Texts in Mathematics 179, Springer- Verlag, 1998. 2. G. J. Murphy, C*-Algebras and Operator Theory, Academic Press, Inc., 1990.


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MATH 716

Harmonic Analysis (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 612, MATH 613 Subjects may include topological groups, abstract harmonic analysis; Fourier analysis, noncommutative harmonic analysis and group representation, automorphic forms, and analytic number theory. Applied harmonic analysis; telecommunications, space exploration, sounds and Fourier optics. Recommended Textbooks: 1. J. Duoandikoetxea, Fourier Analysis, AMS Graduate Studies in Mathematics, Vol.

29, AMS, 2001. 2. L. Grafakos, Classical and Modern Fourier Analysis, Prentice Hall 2003. 3. E. Prestini, The evolution of applied harmonic analysis, Springer

Science+Business Media, LLC, 2004.

MATH 717

Applied Dynamical Systems (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 603 An introduction to discrete and continuous dynamical systems. Topics include: phase space; linear and nonlinear systems; structural stability; classification of equilibrium states, invariant manifolds; Poincare maps, fixed points and period orbits; stability boundaries; local bifurcations; homoclinic orbits; routes to chaos in dissipative systems; applications from physics, biology, population dynamics, economics. Recommended Textbooks: 1. S. Strogatz, Nonlinear Dynamics and Chaos: With Applications to Physics,

Biology, Chemistry and Engineering, 2nd Edition, Westview, 2014. 2. C. Robinson, Dynamical Systems: Stability, Symbolic Dynamics, and Chaos, 2nd

Edition, CRC Press, 1998. 3. Y.A. Kuznetsov, Elements of Applied Bifurcation Theory, 3rd Edition, Springer

Applied Math. Sci. 112, 2010.

MATH 718

Measure theory II (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 603, MATH 612, MATH 613 Borel, Baire and souslin sets, measures on topological spaces, the spaces LP and spaces of measures, general countably additive set function, Radon-Nikodym theorem, covering theorems, differentiability of monotone functions almost everywhere, descriptive definition of the Lebesgue integral, description of Riemann integrable functions, connections between integral and derivative, The Lebesgue integral in n-space. Recommended Textbooks: 1. V. I. Bogachev, Measure theory volume II, Springer, 2007. 2. L. F. Richardson, Measure and Integration: A Concise Introduction to Real

Analysis, John Wiley & Sons, Inc., 2009


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MATH 719

Advanced Topics in Computational and Stochastic PDESs(3 Cr) Compulsory, Credits: 3 (3+0) Prereq: MATH 603, MATH 609 This course will focus on solving linear and nonlinear partial differential equations (PDEs) using finite difference methods and finite elements, also it covers stochastic partial differential equations (SPDEs). The topics are finite difference schemes for PDEs in one and two spatial dimensions, stability analysis, consistency, solving systems of PDEs, dispersion and dissipation analyses. For solving PDEs using finite elements, it covers weighted residual and variational methods, finite element for elliptic problems and higher order elements. For SPDEs; introduction to Malliavin calculus, SPDEs driven by Brownian white noise and Lévy processes, modeling using SPDEs. Recommended Textbooks: 1. J. W. Thomas, Numerical partial differential equations: Finite difference

methods, 2nd Edition, Springer, 1998. 2. A. J. Davies, The finite element method an introduction with partial differential

equations, 2nd Edition, Oxford University Press 2011. 3. H. Holden, B. Øksendal, J. Ubøe and T. Zhang, Stochastic partial differential

equations: A modeling, white noise functional approach, 2nd Edition, Springer Science+Business Media, LLC, 2010.

MATH 720

High Performance Computing (3 Cr) Elective, Credits: 3 (3+0) Prereq: CSCI 101 The course aims at introducing the High Performance Computing Clusters, providing a solid foundation in parallel computer architectures, parallel programming models, application performance monitoring & optimization techniques. This course will discuss fundamentals of parallel systems covering topics ranging from what an HPC cluster consists of to how to efficiently solve complex largescale problems in the areas of computational fluid dynamics, image processing, machine learning and analytics on these systems. Algorithmic theory with hands-on exercises on modern HPC systems, such as Cilk Plus or OpenMP on shared memory nodes, CUDA for graphics co-processors (GPUs), and MPI and PGAS models for distributed memory systems will be introduced. Recommended Textbooks: 1. V. Eijkhout , Introduction to High Performance Scientific Computing. Creative

Commons, 2015. 2. G. Hager and G. Wellein, Introduction to High Performance Computing for

Scientists and Engineers, CRC Press, 2010.

MATH 721

Probabilistic Models and Machine Learning (3 Cr) Elective, Credits: 3 (3+0) Prereq: The course introduces the concepts of probabilistic models and machine learning methods. The course includes the basics Bayesian Decision theory, Generative vs Discriminative modelling. Common Generative models include multivariate Gaussian, Gaussian mixture model (GMM), Multinomial, Markov chain model, n-gram. The course also covers. The course includes Advanced models like hidden Markov model (HMM), Latent Dirichlet Allocation (LDA), Conditional Random Fields (CRF). Recommended Textbooks: 1. D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University

Press, 2012. 2. Kevin P. Murphy and Francis Bach, Machine Learning: A Probabilistic

Perspective, MIT Press, 2012.


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MATH 722

Graph-based Machine Learning (3 Cr) Elective, Credits: 3 (3+0) Prereq: This course focuses on machine learning of graphs coming from networks, e.g., social, biological, technology, etc. The students will learn relevant topics from spectral graph theory, learning theory, bandit theory, necessary mathematical concepts and the concrete graph-based approaches for typical machine learning problems. Topics include learnability on graphs (transductive learning), adaptive online learning with graphs, decision-making on graphs, graph bandits and social networks and recommender systems applications. Finally, the course addresses the scalability of all approaches and deals with huge graphs in practice. Recommended Textbooks: 1. Amarnag Subramanya, Partha Talukdar, Graph-Based Semi-Supervised

Learning, Morgan & Claypool, 2014.


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8.2.5. COURSE CATALOG FOR M.SC. IN ARTIFICIAL INTELLIGENCE

CIE 631 Artificial Intelligence Techniques (3 Cr) Compulsory, Credits: 3 (2+3) Prereq: CIE 205, Data structure and algorithms This course aims at introducing the main concepts in Artificial Intelligence design and implementation for computer systems. The course provides a fundamental understanding of the theory behind the Artificial Intelligence Techniques. Topics includes case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems. Recommended Textbooks:

Stuard Russell and Peter Norvig, Artificial Intelligence. A Modern Approach, 3-rd edition, Prentice Hall, Inc., 2010

CIE 632 Machine Learning Fundamentals (3 Cr) Compulsory, Credits: 3 (2+3) Prereq: CIE 631 The aim of the course is to provide the students with basic concepts in machine learning and teaches them various machine learning problems and methods suitable for them such as supervised vs unsupervised learning, discriminative vs generative learning paradigm, symbolic vs numeric data. Topics include main machine learning techniques including classification, regression, and clustering Recommended Textbooks:

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, Francis Bach. Foundations of Machine Learning (Adaptive Computation and Machine Learning series). The MIT Press, 2012.

CIE 633 Deep Learning Fundamentals (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 632 This course provides the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. Topics include CNN, RNN, LSTM, VAE, GAN, and Reinforcement learning. Recommended Textbooks:

Aaron Courville, Ian Goodfellow, and Yoshua Bengio. Deep Learning. MIT Press, 2016

CIE 634 Natural Language Processing Fundamentals (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 633 This course introduces the highly interdisciplinary area of Artificial Intelligence known alternately as Natural Language Processing (NLP) and Computational Linguistics. The course discusses applications of NLP and the algorithms behind these applications. Applications include automatic translation between languages, extraction and summarization of information in documents, question answering and dialog systems, conversational agents, sentiment analysis, and recommender system. The course focuses on core representations and algorithms as well as prototyping of real-world applications. The course also addresses the usage of deep learning in NLP. Recommended Textbooks:

Steven Bird, Ewan Klein, Edward Loper . Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit 1st Edition, 2009.

Selected Research Papers


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CIE 635 Search Engine (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 634 This course studies the theory, design, and implementation of text-based search engines. Topics include statistical characteristics of text, representation of information needs and documents, several important retrieval models, experimental evaluation, integration of diverse search engines into a single search service, personalized search results, diverse search results, and sponsored search. The course lab component includes design and implementation of large-scale, distributed search engines. Recommended Textbooks:

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze. Introduction to Information Retrieval. Cambridge University Press. 2008

CIE 636 Speech Processing (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 633 This course discusses the practical and theoretical understanding of how computers process a human speech. Topics include speech recognition, speech synthesis and spoken dialog systems. Student will learn to build working speech recognition systems, build their own synthetic voice and build a complete telephone spoken dialog system. At the end of the course students will be able to process real data for real applications, applying statistical and machine learning techniques as well as working with limitations in the technology. Recommended Textbooks:

Xuedong Huang, Alex Acero and Hsiao-wuen Hon . Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Prentice Hall, 2001

CIE 637 Dialogue and Conversational Agents (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 633, CIE 636 The course introduces fundamental concepts in the field of Conversational Agents, collects experiences of researchers working on conversational agents, and reviews techniques for the design and application of conversational agents. The course discusses the successes of and challenges faced by researchers, designers, and programmers who want to use conversational agents for e-commerce, help desks, website navigation, personalized service, and training or education applications. Recommended Textbooks:

Diana Perez-marin and Ismael Pascual-Nieto, Conversational Agents and Natural Language Interaction. IGI Global, 2011


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CIE 638 Deep Learning in Computer Vision (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 633 Computer network This course e addresses how to develop Computer Vision applications by leveraging the power of deep learning. Student will learn different techniques related to object classification, video classification, object detection, image segmentation, captioning, image generation, face analysis, and more. At the end of the course students will master state-of-the-art, deep learning algorithms and their implementation. Recommended Textbooks:

Rajalingappaa Shanmugamani, Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras, Packt Publishing; 1st edition, 2018

CIE 639 Medical Image Processing (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 633 The course introduces the image processing in medicine, emphasizing the clinical relevance and special requirements of the field. Key principles are introduced by implementing algorithms from scratch. Topics include an overview of the physics of medical image processing, image formats, data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction. Recommended Textbooks:

Wolfgang Birkfellner, Applied Medical Image Processing: A Basic Course 2nd Edition. CRC Press, 2014.

CIE 640 Fundamentals of Bioinformatics and Computational Biology (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 633 The course introduces the core topics of bioinformatics. Topics include an introduction to molecular biology and genetics, applying the principles of information technology to challenges in biological data management, sequence analysis, and systems biology, existing biological databases, tools that have become essential in today’s biotechnology research, methodologies for retrieving biological information, fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance, modeling biological sequences and patterns as Markov chains, principles for analyzing and searching for sequences of significant motifs and biomarkers, systems biology, phylogenetic analysis and evolutionary tree computations, gene expression analysis with microarrays. Recommended Textbooks:

Gautam B. Singh. Fundamentals of Bioinformatics and Computational Biology. Springer, 2015.


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CIE 641 Distributed Machine Learning (3 Cr) Elective, Credit: 3(2+3) Prereq: CIE 633 The course introduces the concepts of distributed machine learning and existing frameworks. An example of such framework is Apache Spark. The course introduces the basics of Spark's API used to load and process data and prepare the data to use as input to the various machine learning models. Common machine learning models are revisited to scale it up to work with large scale data. Common machine learning models include recommender systems, classification, regression, clustering, and dimensionality reduction. The course also covers advanced topics such as working with large-scale text data, and methods for online machine learning and model evaluation using Spark Streaming. Recommended Textbooks:

Nick Pentreath, Machine Learning with Spark - Tackle Big Data with Powerful Spark Machine Learning Algorithms. Packt Publishing, 2015

CIE 650 Advanced Topics in Artificial Intelligence (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 633 This course will introduce the student to cutting edge and specialized topics within the field of Artificial Intelligence. Topics will vary from year to year but will focus mainly on new techniques and methodology for solving AI problems and new applications for AI. Recommended Textbooks:

Research Papers

8.2.6. COURSE CATALOG FOR M.SC. IN CYBER SECURITY

CIE 601 Computer Security (3 Cr) Compulsory, Credits: 3 (2+3) Prereq: CIE 302, Operating System CIE 406, Computer Networks This course aims at introducing the principles of computer security and how various security attacks and countermeasures. Students will be able to design and implement software systems and applications that are secure against attacks. They will also be able to evaluate the risks faced by computer and network systems, detect common vulnerabilities in software, use proper methods to protect their systems and networks, and apply the learned security principles to solve real-world problems. Recommended Textbooks:

Wenliang Du. Computer Security, A hands on Approach. CreateSpace, 2017.


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CIE 602 Network Security (3 Cr) Compulsory, Credits: 3 (2+3) Prereq: CIE 601 CIE 406, Computer network The course provides a solid understanding of the design and analysis of network security architectures, protocols, and services. The course provides in-depth coverage of today's network security standards, their functionality and limitations e.g., SSL/TLS, Kerberos, IPsec, OAuth, WPA. In the course, we will discuss recent trends in network security attacks, and cyber-attacks in general, and analyze a variety of attacks from the analysis of worms spreading, to SSL/TLS session renegotiation/compression, DNS security, to spam and it's crypto-based countermeasures. The laboratory assignments include networks scanning, host/network intrusion detection, buffer overflow attacks, password cracking, sql injection, and cross site scripting. Recommended Textbooks:

Charles Kaufman, Radia Perlman, Mike Speciner. Network Security: Private Communication in a Public World. Pearson Education, 2002

CIE 603 Cryptography (3 Cr) Compulsory, Credits: 3 (2+3) Prereq: CIE 602, MATH 308 This course introduces the principles and practice of cryptography and network security. Topics include cryptographic methods, hash functions, key exchange, secure communication, message authentication, digital signatures, network security, system security, modern day security protocols and standards. Recommended Textbooks:

William Stallings. Cryptography and Network Security: Principles and Practice, Prentice Hall, 6th edition, 2013

CIE 604 Cyber Security Legal Aspects & Ethical Concerns (3 Cr) Compulsory, Credits: 3 (3+0) Prereq: CIE 603 This course discusses the impact of cyber ethics and cyber law on information technologies and society. The course highlights the ethical and legal practices used in computing technologies, increase the effectiveness of computing students and professionals in applying ethical values and legal statues, and provide insight on ethical and legal discussions of real-world applications. Topics include computer crime, ethics and legal aspects of virtual worlds, intellectual property, networking, online business applications, online privacy and security, virtual law and web accessibility Recommended Textbooks:

Alfreda Dudley, James Braman and Giovanni Vincenti. Investigating Cyber Law and Cyber Ethics: Issues, Impacts and Practices. IGI Global, 2011.


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CIE 605 Computer Forensics (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 604 The course covers both the principles and practice of digital forensics. Topics include Societal and legal impact of computer activity: computer crime, intellectual property, privacy issues, legal codes; risks, vulnerabilities, and countermeasures; methods and standards for extraction, preservation, and deposition of legal evidence in a court of law. The course addresses network and Mobile network forensic in addition to computer forensic. Recommended Textbooks:

Marjie T. Britz, Computer Forensics and Cyber Crime: An Introduction. 3rd Edition. Pearson, 2013

CIE 606 Cloud Computing Security (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 604 CIE 406, Computer networks This course addresses the security and privacy issues in Cloud Computing systems. The course provides an overview of current best practices and available technologies. The course examines the Cloud Computing model, the threat model, and security issues related to data and computation outsourcing, and explores practical applications of secure Cloud Computing. Recommended Textbooks:

Vic Winkler, Securing the Cloud, Syngress, 2011

CIE 607 Critical Infrastructure Protection (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 606 This course introduces the policy, strategy, and practical application of critical infrastructure security and resilience (CISR) from an all-hazards perspective. Topics include the assessments of the 21st century risk environment and its application to the CISR mission area, analyzing CISR partnership frameworks, information sharing processes and systems, and coordination/collaboration challenges, and comparing different strategic approaches regarding critical infrastructure risk management. Recommended Textbooks:

Lewis, Ted G. (ed.), Critical Infrastructure Protection in Homeland Security: Defending a Networked Nation, Second Edition, John Wiley & Sons, Inc., 2015.

CIE 608 Software Reverse Engineering and Malware Analysis (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 604 This course covers fundamental problems, principles, and techniques in software reverse engineering of binaries. Topics include static analysis techniques, disassembly algorithms, dynamic analysis techniques, malware analysis techniques, anti-analysis techniques, and malware obfuscation and packing techniques. It also involves research opportunities to analyze new malware samples and firmware and to develop new analysis tools. Recommended Textbooks:

Michael Sikorski and Andrew Honig. Practical Malware Analysis: A Hands-On Guide to Dissecting Malicious Software. No Starch Press, 2012.


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CIE 609 Disaster Recovery and Business Continuity (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 604 This course covers the concepts, strategies, and implementation of Business Continuity and IT Disaster Recovery Plans. Topics include strategies to protect the company and organizations, its’ assets, its’ ability to do business under any circumstance, and to be resilient. After completing this course, students should have a general knowledge about Business Continuity and IT Disaster Recovery and be able to address the key areas in this field. Recommended Textbooks:

Michael Sikorski and Andrew Honig. Business Continuity and Disaster Recovery Planning for IT Professionals. Syngress, 2013.

CIE 610 Intrusion Detection Systems (3 Cr) Elective, Credit: 3(2+3) Prereq: CIE 604 This course provides a hands-on exposure to the mathematical principles and techniques used in intrusion detection. Students will experiment with the real-life process of going from a theoretical intrusion detection solution to its implementation. Student will gain understanding of basic Issues, concepts, principles, and techniques in Intrusion Detection. They will be able to evaluate Intrusion Detection Systems for particular security requirements. Recommended Textbooks:

Roberto Di Pietro (Editor), Luigi V. Mancini (Editor). Intrusion Detection Systems. Springer, 2008.

CIE 611 Securing the Internet of Things (3 Cr) Elective, Credits: 3 (2+3) Prereq: CIE 610 The course provides both the theoretical and practical knowledge regarding security in the Internet of Things (IoT). Topics include the fundamental concepts of IoT security, practical solutions that account for resource limitations at IoT end-node, hybrid network architecture, communication protocols, application characteristics, and the most important potential IoT security risks and threats. Recommended Textbooks:

Shancang Li and Li Da Xu, Securing the Internet of Things, Syngress, 2017

CIE 612 Data Hiding (3 Cr) Elective, Credit: 3(2+3) Prereq: CIE 604, CIE 552 This course Teach students theoretical aspects of the watermarking and steganography. Students will learn the embedding process in steganography. Student will possess the passion for acquiring knowledge and skill in preserving authentication of Information. They will be able to identify theoretic foundations of steganography and steganalysis and differentiate between watermarking and steganography. Topics include information hiding, models of watermarking, watermark security, Steganography, and Steganalysis. Recommended Textbooks:

Ingemar Cox, Matthew Miller, Jeffrey Bloom, and Jessica Fridrich, “Digital Watermarking and Steganography”, 2nd Edition, The Morgan Kaufmann Series in Multimedia Information and Systems, 2007.


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CIE 620 Advanced Topics in Cybersecurity (3 Cr) Elective, Credits: 3 (3+0) Prereq: CIE 604 This course will introduce the student to cutting edge and specialized topics within the field of cyber-security. Topics will vary from year to year, but will focus mainly on network perimeter protection, host-level protection, authentication technologies, intellectual property protection, formal analysis techniques, intrusion detection and similarly advanced subjects. Recommended Textbooks:

Research Papers


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8.2.7. COURSE CATALOG FOR M.SC. IN NANOTECHNOLOGY AND NANOELECTRONICS

ENGINEERING

NANENG 601 Advanced VLSI Design Compulsory, Credits: 3 (3+0) Prereq: The course covers advanced different topics in VLSI that includes the CMOS fabrication, layout, and design rules. In addition, some advanced topics on the analog VLSI such as MOS amplifiers, oscillators, phase locked loops, and data converters. The course also covers some advanced topics on digital VLSI such as CMOS digital circuits and VHDL. Some selected topics on the VLSI are introduced such as RF design specifications, power management, and embedded systems design that includes IoT battery operated systems. The course also introduces several CAD tools through the course mini-projects.

Text: -CMOS VLSI Design: A circuits and systems perspective, Neil

H.E. Weste, David Harris, 4th Edition, Pearson, 2015.

Other references: -Wayne Wolf, Modern VLSI Design: IP-Based Design,

PRENTICE HALL, 2008

- Yuan Taur, Tak H. Ning, Fundamentals of Modern VLSI Devices, Cambridge University Press, 2013

NANENG 602 Advanced Electromagnetics and Lightwave Propagation Compulsory, Credits: 3 (3+0) Prereq:

The course builds on undergraduate electromagnetics to systematically develop advanced concepts in electromagnetic theory for engineering applications. The course will present a review of transmission line and use of Smith Chart for admittance. Additionally, the wave equation and general solutions for TEM, TE, and TM waves will be presented. Further, the different types of waveguides such as parallel plate waveguide, rectangular waveguide, Circular Waveguide, Coaxial Line, Slab-Waveguide and metallic waveguides will be studied. Additionally, periodic structures, network theory, passive microwave Elements, microwave filters, microwave amplifiers and antennas will be covered.

Text: -Microwave Engineering, Book by David M Pozar, Wiley, Aug

12, 1997 - Technology & Engineering

Other references: - Microwave Engineering: Concepts and Fundamentals,

Ahmad Shahid Khan March 29, 2017 by CRC Press

NANENG 603 Engineering Mathematics and Computational Methods Compulsory, Credits: 3 (3+0) Prereq: This course provides a review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: differential equations of equilibrium; Laplace's equation and potential flow; boundary-value problems; minimum principles and


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calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications. Moreover, the course will emphasize the development of numerical algorithms to provide solutions to common problems formulated in science and engineering. The emphasis of the course will be the study of numerical algorithms to understand (1) the guaranteed accuracy that various methods provide, (2) the efficiency and scalability for large scale systems, and (3) issues of stability.

Text: -Strang, Gilbert. Computational Science and Engineering. Wellesley, MA: Wellesley-Cambridge - Numerical Analysis 10th Edition, by Richard L. Burden

(Author), J. Douglas Faires

NANENG 604 Advanced Devices and Semiconductors Compulsory, Credits: 3 (3+0) Prereq: This course covers physical principles of semiconductors, band theory, transport properties, characteristics and models of various modern semiconductor devices including: FINFET, Double gate and Nanowire FETs. Also, covers the 1D/2D electrostatics modeling for FINFET, DG, GAA and SPIN based devices. Both classical and quantum models will be covered. The technology road maps according to the new promising devices will be covered.

Text: 1-Advanced Semiconductor Fundamentals, Robert F. Pierret , 2nd

Ed, Pearson, 2002 2- Physical Properties of Semiconductors, Charles M. Wolfe , kciN Holonyak, cronerP ,. aeciitSg E yrogeci-3SiiE llaH . 3-FinFET and Other Multi-Gate Transistors, by J.-P. Colinge, Springer, 2007. 4- Variation Aware Analog and Mixed-Signal Circuit Design in Emerging Multi-Gate CMOS Technologies, by M. Fulde, Springer, 2010.

NANENG 605 Advanced Nanofabrication and MEMS Compulsory, Credits: 3 (3+0) Prereq: This course addresses several advanced techniques and fabrication process flows for nanodevices, including the materials used. Topics include Metal/High-k process integration, Atomic Layer Deposition (ALD), metal-organic CVD, advanced optical lithography practices, electron beam lithography, Cu interconnects, high density plasma processes. It covers as well advanced topics related to the design and fabrication of Microelectromechanical systems. Throughout the course, various transduction mechanisms from any physical, chemical or biological domain into an electrical domain are presented. The fabrication techniques as applied to MEMS are also covered in this course. The methodology for building a characterization setup for different types of MEMS devices is also covered.

Text: -Fabrication Engineering at the Micro and Nanoscale, S. Campbell,

4th Ed. , Oxford University Press, 2008. -Handbook of Modern Sensors, Physics, Design and Applications


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Fraden, 2015, 5th Edition Other Reference: - Fundamentals of Microfabrication and Nanotechnology, Marc Madu, CRC press, third edition 2011. - Foundations of MEMS, Chang Liu, Second Edition, Pearson, 2012 -Post-Processing Techniques for Integrated MEMS, Sherif Sedky, Publisher: Artech House, 2005.

Elective Courses Classes from other programs may be counted as electives, by approval of supervision committee NANENG 606 Advanced Analog MOS VLSI Design

Compulsory, Credits: 3 (3+0) Prereq: The course starts from the basic undergraduate analog design courses to develop good knowledge of the analog integrated circuits design challenges that include the noise, nonlinearity, and device mismatch and their impact on different analog circuits such as amplifiers, common-mode feedback circuits, switched capacitors, voltage references, data converters, and phase locked loops. The course lectures are given along with complete post layout simulations class assignments using Cadence Spectre and Mentor Graphics Calibre up to the tape-out level.

Text: -Behzad Razavi, Design of Analog CMOS Integrated Circuits,

McGraw Hill, 2016. Other references: -Tony Chan Carusone, David Johns and Ken Martin, Analog Integrated Circuit Design, 2nd Edition, John Wiley &

Sons,2011. -Paul R. Gray, Paul J. Hurst, Stephen H. Lewis, and Robert G. Meyer, Analysis and Design of Analog Integrated Circuits, 5th

edition NANENG 607 Advanced Digital CMOS VLSI Design

Compulsory, Credits: 3 (3+0) Prereq:

Design of CMOS digital integrated circuits at the transistor level. Related topics include MOSFET switch and I-V models, logic gate design, transistor sizing, interconnect parasitics, gate delay, timing design, logical effort, static and dynamic logic families, arithmetic structures, latch and flip-flop elements, memory cells and arrays, and input/output circuitry. Moreover, the course includes in addition to the custom design, the ASIC design using standard cells as well as the FPGA design. All supporting CAD tools will be integrated in the course class work such as Cadence Spectre, Mentor Graphics Calibre, Synopsis Design Compiler, Cadence SoC Encounter, and Xilinx ISE and Vivado.

Text: -Digital Integrated Circuit: A Design Perspective, Jan Rabaey, A. Chndrakasan, and B. Nikolic, 2nd Edition, Printice Hall, 2008. Other references: - Neil H. E. Weste, and David F. Harris, CMOS VLSI Design: A

Circuits and Systems Perspective, Pearson/Addison-Wesley, 2005 -Harry J.M. Veendrick, Nanometer CMOS ICs: From Basics to ASICs, Springer 2017.


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NANENG 608 Advanced Electronic Nano-Devices Compulsory, Credits: 3 (3+0) Prereq:

This course covers physical principles, characteristics and different transport models (e.g., drift diffusion, hydrodynamic, and ballistic transports) of various modern semiconductor devices including: Tunnel FET, strained materials based FET devices, III/V based devices and their effects on device performance. Also, selected energy harvesting devices such as PV, Thermoelectric generators/coolers and piezoelectric device will be covered and modeling will be covered. The technology road maps according to the new promising devices will be covered.

Text: 1- Fundamentals of III-V Semiconductor MOSFETs”, by Oktyabrsky, Serge, ISBN 978-1- 4419-1547- 4 2- FinFET and Other Multi-Gate Transistors, by J.-P. Colinge, Springer, 2007

NANENG 609 Data Converters Compulsory, Credits: 3 (3+0) Prereq:

This course explores fundamentals of data converter systems and circuits. Most of the commonly used ADC architectures such as flash, two-step, pipelined, algorithmic, successive-approximation and integrating ADCs as well as oversampling delta-sigma modulators are discussed in details. The effects of circuit non-idealities are analyzed and various system and circuit techniques will be discussed to enhance the performance of these converters. The course objective is to provide a thorough background of data converter systems and circuits, discuss the real world applications, IC design challenges and prepares students for other areas of analog and digital IC design.

Text: Behzad Razavi, Principles of Data Conversion System Design, Wiley, 2005.

Other references: - Walter Allan Kester, The Data Conversion Handbook, Elsevier,

2005. - Shanthi Pavan, Richard Schreier, and Gabor C., Understanding Delta-Sigma Data Converters, Wiley 2017

NANENG 610 RF Design Compulsory, Credits: 3 (3+0) Prereq:

The course will cover the design and analysis of radio frequency integrated circuits (RFICs) for communications. We will begin with an overview of RF and wireless technology, and cover some fundamental concepts in RF design such as nonlinearity, sensitivity, and dynamic range. Matching and impedance transformation networks will be discussed, as well as S-parameters. Following this we will discuss transceiver architectures (Heterodyne, Direct Conversion, etc.), and review modulation and up-conversion concepts. The latter half of the course will be devoted to a detailed examination of each of the blocks in the transceiver architectures discussed: Low Noise Amplifiers, Mixers, Oscillators, Frequency Synthesizers, and Power Amplifiers.


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Text: • Behzad Razavi, RF Microelectronics, Pearson Education International,

2012 Other references: • Thomas H. Lee, The Design of CMOS Radio-Frequency Integrated

Circuits, 1998. NANENG 611 Advanced Computer Architecture

Compulsory, Credits: 3 (3+0) Prereq: This course will cover advanced topics in computer architectures focusing on emerging uniprocessor and multiprocessor architectures, implementation issues (architect's perspective) in deep submicron CMOS, as well as nanoscale fabrics and architectures based on new types of emerging devices.

Text: -Hennesy and Patterson, Computer Architecture A Quantitative

Approach. - J. P. Shen and Mikko H. Lipasti, Modern Processor Design.

NANENG 612 Formal Verification Compulsory, Credits: 3 (3+0) Prereq: This course aims to familiarize students with main classes of FV techniques that are likely to become most widespread in industry in the coming years. It will provide sufficient background in FV, its usage and deployment techniques. The course provides methodologies to guarantee Correctness assurance in the design of systems, especially those of life or social critical functions. “Testing can at best show the presence of errors, but never their absence”. Formal techniques treat a system as a mathematical object and relate its behavior with its structure through mathematical reasoning (i.e. proofs). In this course, we will study how to specify the behavior of a system formally and how to establish its correctness rigorously. With an introduction to discrete math, set theory, and temporal logic, we will study how to specify and verify systems using the TLA+ language and tools

Text: -M. Huth and M. Ryan: Logic in Computer Science: Modelling and Reasoning about Systems, 2nd Edition, Cambridge University Press, 2004 (H&R). - Bushnell and Agrawal, Essentials of Electronic Testing for Digital, Memory & Mixed-Signal Circuits, Kluwer Academic

Publishers, 2000

NANENG 613 Neuromorphic Computing Compulsory, Credits: 3 (3+0) Prereq: In recent years, both industry and academia have shown large interest in low-power hardware designs for neuromorphic computing (e.g. TrueNorth) and deep learning algorithms (e.g. convolutional neural networks) for a wide range of image, speech, and biomedical applications. In this course, we will learn the underlying theory, basic algorithms, and efficient device/circuit/architecture design of neuromorphic computing.

Text: - “Event-Based Neuromorphic Systems,” by Shih-Chii Liu, Tobi Delbruck, Giacomo Indiveri, Adrian M. Whatley, and Rodney


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Douglas, 2015. NANENG 614 Networks on chip

Compulsory, Credits: 3 (3+0) Prereq: Interconnection Networks refer to the communication fabric interconnecting various components of a computer system. They occur at various scales – from on-chip networks (OCN)/Networks-on-Chip (NoCs) in billion-transistor many-core chips, to custom high-speed wired networks in HPC supercomputers, to optical fiber networks within datacenters. The growing emphasis on parallelism, distributed computing, and energy-efficiency across all these systems makes the design of the communication fabric critical to both high-performance and low power consumption. This course examines the architecture, design methodology, and trade-offs of interconnection networks.

Text: -N. E. Jerger and L.-S Peh, “On-Chip Networks,” Morgan Claypool

Publishers, 2009 -Networks on Chip, edited by Axel Jantsch, Hannu Tenhunen, Kluwer Academic Publishers, Boston.

NANENG 615 Power Management and High Temperature Power Devices Compulsory, Credits: 3 (3+0) Prereq: This course introduces different circuits related to power management systems. Topics include analysis and design of voltage references, magnetics, and different dc-dc converters including: switched-mode power converters, linear regulators and switched-capacitor charge pumps.

Text: -Fundamentals of Power Semiconductor Devices, B.J.Baliga, Springer Science, 2008 -Power Electronics: Circuits, Devices & Applications,

Muhammad H. Rashid, 4th Edition, Pearson, 2013 -Fundamentals of Power Electronics 2nd Edition, by R. W.

Erickson and D. Maksimovic, Kluwer Academic Publisher, 2001

NANENG 616 Nanophotonics Compulsory, Credits: 3 (3+0) Prereq: The course is designed to introduce the latest developments in the newly emerging field of nano-photonics. The students will gain understanding of the fundamental physics governing the interaction between light and nanoscale materials. The students will also be exposed to the various novel optical phenomena observable in the nanoscale and their applications. Specific topics to be discussed will include photonic crystals, plasmonics and metamaterials

Text: - Photonic Crystals: Molding the Flow of Light (second edition) By John D. Joannopoulos, Published in 2008 by Princeton University Press - Plasmonics: Fundamentals and Applications By Maier, Stefan Alexander, Springer US - Principles of Nano-Optics, By Lukas Novotny and Bert Hecht, 2nd Edition, Bert Hecht, Cambridge, September 2012 - Optical Metamaterials: Fundamentals and Applications By Cai,


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Wenshan and Shalaev, Vladimir, Springer International Publishing, 2009

NANENG 617 Optical fiber Communications Compulsory, Credits: 3 (3+0) Prereq: This course will present a comprehensive description of the technology of fiber optical communication system. Additionally, a balanced discussion between component operation and system design consideration will be covered. Further, performance parameters and fabrication problems, lasers, LED modulation and detector responses will be introduced. Furthermore, link budget analysis as well as advantages of fiber optics, recent developments and applications will be studied.

Text: -Optical Fiber Communications 4th Edition, by Gerd

Keiser Optical Fibers: Structures, Waveguiding, and Fabrication

Other References: -Joseph Palais, Fiber Optic Communications, fourth edition, Prentice Hall, 1998

NANENG 618 Photonic Devices and Systems Compulsory, Credits: 3 (3+0) Prereq:

This course aims to present a comprehensive introduction to the field of optical guided waves and photonic devices and systems for optoelectronic engineers, optical communication engineers and physicists. It incorporates the topic of integrated optics and provides a balance between theoretical foundations and practical applications.

Text: - Optical Guided Waves and Devices Hardcover – September 1,

1992 by Richard Syms (Author), Jeeg CenogJ

NANENG 619 Computational Photonics Compulsory, Credits: 3 (3+0) Prereq:

This course provides a comprehensive coverage of modern numerical modelling techniques for designing photonic devices for use in modern optical telecommunications systems. In addition, the course will present the state-of-the-art in computational photonics techniques, covering methods such as full-vectorial finite-element beam propagation, bidirectional beam propagation, complex-envelope alternative direction implicit finite difference time domain, multiresolution time domain, and finite volume time domain. The course guides the students through the concepts of modelling, analyzing, designing and optimizing the performance of a wide range of photonic devices by building their own numerical code using these methods.

Text: -Fundamentals of Optical Waveguides, Katsunari Okamoto, Academic Press, 2006 - Computers - 561 pages - S. S. A. Obayya, Computational Photonics, John Wiley & Sons,

Oct. 2010 NANENG 620 Advanced micro optical systems


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This course covers advanced topics related to optical microsystems, optical transducers and sensors. Interaction of optical waves with inhomogeneous media is cover, which is important for modeling the sensing mechanisms. Advanced topics related to coupling between different domains such as Optical-Mechanical coupling is covered, along with their practical application for realizing optical tools and equipment.

Text: -Saleh, B. E. A.; Teich, M. C. Fundamentals of Photonics, John Wiley & Sons: USA, 1991, 0-471- 83965-5. Other Reference: - Yariv, A. Quantum electronics, 3rd ed.; John Wiley &

sons: USA, 1989, 0-471- 60997-8. - Olav Solgaard, Photonic Microsystems, Micro and Nanotechnology Applied to Optical Devices and Systems, 2009 Springer Science + Business Media, ISBN: 978-0- 387-29022- 5.

NANENG 621 Advanced Photovoltaics and Photonic Nano-devices Compulsory, Credits: 3 (3+0) Prereq:

The wide point of this course is to portray the working standards of present day photovoltaic and photonic devices with a deep understanding of the factor limiting its performance with emphasize on the interior structure, working conditions and the physics behind it. Advanced devices technologies are then described in detail with their output characteristics. These include photovoltaic devices, light emitting diodes, semiconductor lasers and finally photodetectors and photoconductors.

Text: -Advanced in multiphoton processes and spectroscopy by S. H.

Lin, A. A. Villaeys Y. Fuiimura -Handbook of Advanced Electronic and Photonic materials and Devices by Hari Singh Nalwa -Optoelctronics and photonics principal and practices, second

edition S. O. Kasap NANENG 622 Selected topics in Nanofabrication


This course highlights the most promising materials and techniques that have been used recently for nanofabrication. The new trends in materials processing and applications will be discussed especially those which satisfy the needs of semiconductors industry. We will focus on the 2D materials, atomic scale processing, and the most advanced patterning techniques.

Text: 1. MICHEL HOUSSA, ATHANASIOS DIMOULAS, ALESSANDRO

MOLLE, “2D MATERIALS FOR NANOELECTRONICS”, CRC PRESS, 2016.


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2. PHAEDON AVOURIS, ,ZkI H. 3,YkOE TONY LOW, “2D MATERIALS: PROPERTIES

AND DEVICES”, CAMBRIDGE UNIVERSITY PRESS, 2017. 3. NICOLA PINNA, MATO KNEZ, “ATOMIC LAYER DEPOSITION OF NANOSTRUCTURED MATERIALS”, WILEY, 2012. 4. YING WANG AND GUOZHONG CAO, ”NANOSTRUCTURES AND NANOMATERIALS: SYNTHESIS, PROPERTIES, AND

APPLICATIONS” (2ND EDITION), WORLD SCIENTIFIC PUBLISHING CO., 2011. 5. FERNANDO A. LASAGNI AND ANDRÉS F. LASAGNI,

“FABRICATION AND CHARACTERIZATION IN THE MICRO-NANO RANGE: NEW

TRENDS FOR TWO AND THREE-DIMENSIONAL STRUCTURES”, SPRINGER, 2011. 6. DAVID L. ANDREWS, GREGORY D. SCHOLES, AND GARY P.

WIEDERRECHT, “COMPREHENSIVE NANOSCIENCE AND TECHNOLOGY”,

ELSEVIER , 2011. NANENG 623 Modeling of Nanofabrication Processes and Equipment


This course covers the mathematical models used to describe basic processes in nanofabrication and equipment. The focus is on the interaction of processes with the fabrication equipment configurations, through first order equations and FDTD / finite element methods. Topics include diffusion, gas phase and surface reactions, deposition, plasma, lithography processes, vacuum systems and pumps, and practical reactor design.

Text: - Fabrication Engineering at the Micro and Nanoscale, S. Campbell, 4th Ed. , Oxford University Press, 2008. - ATOMIC LAYER DEPOSITION OF NANOSTRUCTURED

MATERIALS, WILEY, 2012 - HIGH PERMITTIVITY GATE DIELECTRIC MATERIALS,

SPRINGER, 2013 - FUNDAMENTAL PRINCIPLES OF OPTICAL LITHOGRAPHY, C.

MACK, WILEY, 2007 - PRINCIPLES OF PLASMA DISCHARGES AND MATERIALS

PROCESSING, WILEY, 2005 NANENG 624 Physical Properties of Electronic Materials


This course covers advanced physical and electronic properties in semiconductors and electronic materials such as piezoelectricity, superconductivity, phonons, dielectrics, ferromagnetism. Advanced transport theory using Boltzmann transport equation, including different scattering mechanisms, band diagram calculation methods are also discussed. Quantum mechanical modeling is used across the course.

Text: - Introduction to Solid State Physics, Charles Kittel, 8th Ed., Wiley, 2008


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- Physical Properties of Semiconductors, Charles M. Wolfe , Nick Holonyak, cronerP ,. aeciitSg E yrogeci-3SiiE llaH.

NANENG 625 Advanced Real-time embedded systems Compulsory, Credits: 3 (3+0) Prereq:

The course covers the integrated hardware and software aspects of embedded processor architectures, along with advanced topics such as real-time, resource/device and memory management. Students can expect to learn how to program with the embedded architecture that is ubiquitous in cell-phones, portable gaming devices, robots, tablets, etc. Students will then go on to learn and apply real-time principles that are used to drive critical embedded systems like automobiles, avionics, medical equipment, wearables, etc. Topics covered include embedded architectures interaction with devices; concurrency; real-time principles; implementation trade-offs, profiling and code optimization; embedded software. Students will acquire skills in the design/implementation/debugging of core embedded real-time functionality. Novel topics like usage of neural networks for embedded control is also exposed.

Text: - Computers as Components: Principles of Embedded Computing System Design Wayne Wolf

NANENG 626 Selected topics on EDA algorithms Compulsory, Credits: 3 (3+0) Prereq:

Beginning with a general introduction to VLSI design flow and Electronic Design Automation (EDA) tools, the course mainly focuses on VLSI physical design (layout). It covers partitioning, placement, floor planning, routing (global and detailed), and compaction. We will discuss why and how to partition a design process into sub-problems and will study how to design good algorithms to solve each of those sub-problems. The course will also introduce numerical methods for large scale simulation of electronic analog/mixed signals using SPICE, compact models and matrix solution. Novel research areas like OPC will also be exposed.

Text: -Physical Design Essentials, Khosrow Golshan, Springer

NANENG 627 Selected topics in MEMS/NEMS Compulsory, Credits: 3 (3+0) Prereq:

This course addresses advanced topics related to the design and fabrication of Microelectromechanical systems. Throughout the course, various transduction mechanisms from any physical, chemical or biological domain into an electrical domain are presented. The fabrication techniques as applied to MEMS are also covered in this course. The methodology for building a characterization setup for different types of MEMS devices is also covered.

Text: -Post-Processing Techniques for Integrated MEMS, Sherif Sedky, Publisher: Artech House, 2005


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- Handbook of Modern Sensors, Physics, Design and Applications Fraden, 2015, 5th Edition - Research papers

8.2.8. COURSE CATALOG FOR PHD IN NANOTECHNOLOGY AND NANOELECTRONICS ENGINEERING

NANENG 706

Selected topics in VLSI Design Elective, Credits: 3 (3+0) This course discusses advanced topics VLSI Design

NANENG 707

Selected topics in Electromagnetics and Light wave Propagation Elective, Credits: 3 (3+0) This course discusses advanced topics in Electromagnetics and Light wave Propagation

NANENG 708 Selected topics in Nanofabrication and MEMS Elective, Credits: 3 (3+0) This course discusses advanced topics in Nanofabrication and MEMS

NANENG 709 Selected topics in Devices and Semiconductors Elective, Credits: 3 (3+0) This course discusses advanced topics in Devices and Semiconductors

NANENG 710 Selected Topics in Analog MOS VLSI Design Elective, Credits: 3 (3+0) This course discusses advanced topics in Analog MOS VLSI Design

NANENG 711 Selected Topics in Electronic Nano-Devices Elective, Credits: 3 (3+0) This course discusses advanced topics in Electronic Nano-Devices

NANENG 712 Selected Topics in Digital MOS VLSI Design Elective, Credits: 3 (3+0) This course discusses advanced topics in Digital MOS VLSI Design

NANENG 713 Selected Topics in Data Converters Elective, Credits: 3 (3+0) This course discusses advanced topics in Data Converters

NANENG 714 Selected Topics in RF design Elective, Credits: 3 (3+0) This course discusses advanced topics in RF design

NANENG 715 Selected Topics in Computer Architecture Elective, Credits: 3 (3+0) This course discusses advanced topics in Computer Architecture

NANENG 716 Selected Topics in Nanophotonics Elective, Credits: 3 (3+0) This course discusses advanced topics in Nanophotonics

NANENG 717 Selected Topics in Photonic Devices and Systems Elective, Credits: 3 (3+0) This course discusses advanced topics in Photonic Devices and Systems

NANENG 718 Selected Topics in Computational photonics Elective, Credits: 3 (3+0) This course discusses advanced topics in Computational photonics

8.2.9. COURSE CATALOG FOR M.SC. IN ENVIRONMENTAL ENGINEERING


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ENV 601 Environmental Sciences (3 Cr) Compulsory, Credits: 3 (3+0) Basic principles of chemistry, physical chemistry, organic chemistry, biochemistry, microbiology, plant biology and ecology. Recommended Textbooks: Wright, J. Environmental Chemistry. Routledge, 2004. Rana, S.V.S. Essesntials of Ecology and Environmental Science. PHI Learning

Pvt. Ltd, 2013. Calver, M., A. Lymbery, and J. McComb. 2009. Environmental Biology.

Cambridge University Press, 2009.

ENV 602 Environmental Data Analysis and Statistics (3 Cr) Compulsory, Credits: 3 (3+0) This course covers quantitative analysis of uncertainty and risk for engineering applications. Fundamentals of probability, random processes, statistics, and decision analysis are covered along with random variables and vectors, uncertainty propagation, conditional distributions, and second-moment analysis. System reliability is introduced. Other topics covered include Bayesian analysis and risk-based decision, estimation of distribution parameters, hypothesis testing, simple and multiple linear regressions, and Poisson and Markov processes. Emphasis is placed on environmental engineering applications. Recommended Textbooks: Rice, J.A. Mathematical Statistics and Data Analysis. Duxbury Press, 2006. Acevedo, M.F. Data Analysis and Statistics for Geography, Environmental

Science, and Engineering. CRC Press, 2012.

ENV 603 Environmental Systems Modeling (3 Cr) Compulsory, Credits: 3 (3+0) This course introduces the basic approaches for modeling environmental systems. Impacts from anthropogenic activities to the environment are systematically evaluated via the use of various simulation approaches. Case studies of complex environmental systems are incorporated to enhance the integrated skills available for model synthesis via multidisciplinary analysis. Recommended Textbooks: Deaton, M.L., and J.J. Winebrake. Dynamic Modeling of Environmental Systems.

Springer Science & Business Media, 2012.

ENV 604 Transport Phenomena Modeling (3 Cr) Compulsory, Credits: 3 (3+0) This course discusses movement and transformation of substances released in the natural environment. Topics include modeling advection, dispersion and reaction; aggregation and parameterization of various mixing processes; role of heterogeneity, anisotropy, and stratification in natural media; and applications to atmospheric, surface water and groundwater pollution problems. Recommended Textbooks: Schnoor, J.L. Environmental Modeling - Fate and Transport of Pollutants in

Water, Air, and Soil. Wiley-Interscience, 1996. Plawsky, J.L. Transport Phenomena Fundamentals, Third Edition. 3rd ed.

Chemical Industries. CRC Press, 2014.


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ENV 605 Unit Operations in Environmental Engineering (3 Cr) Compulsory, Credits: 3 (3+0) This course develops understanding of quantitative and engineering principles related to physical, chemical, and biological unit processes in environmental engineering. Students learn and apply engineering design principles for unit operations, including design methodology, design criteria, regulatory requirements, economic considerations, and design alternatives. Recommended Textbooks: Louis T., R.R. Dupont, and K. Ganesan. Unit Operations in Environmental

Engineering. Wiley, 2017.

ENV 620 Aquatic Chemistry (3 Cr) Elective, Credits: 3 (3+0) This course details the quantitative treatment of chemical processes in aquatic systems such as lakes, oceans, rivers, estuaries, groundwaters, and wastewaters. It includes a brief review of chemical thermodynamics that is followed by discussion of acid-base, precipitation-dissolution, coordination, and reduction-oxidation reactions. Emphasis is on equilibrium calculations as a tool for understanding the variables that govern the chemical composition of aquatic systems and the fate of inorganic pollutants. Recommended Textbooks: Brezonik, P., and W. Arnold. Water Chemistry: An Introduction to the Chemistry

of Natural and Engineered Aquatic Systems. Oxford University Press, 2011. Morel, Francois M., and Janet G. Hering. Principles and Applications of Aquatic

Chemistry. New York, NY: Wiley-Interscience, 1993. Anderson, G. W. Thermodyanmics of Natural Systems. New York, NY: Wiley-

Interscience, 2005. Stumm, Werner, and James J. Morgan. Aquatic Chemistry. New York, NY:

Wiley-Interscience, 1996.

ENV 621 Environmental Microbiology (3 Cr) Elective, Credits: 3 (3+0) This course provides an introduction to the diverse roles of microorganisms in natural and artificial environments. The course covers topics including: cellular architecture, energetics, and growth; evolution and gene flow; population and community dynamics; water and soil microbiology; biogeochemical cycling; and microorganisms in biodeterioration and bioremediation. Recommended Textbooks: Pepper, I.L., C.P. Gerba, and T.J. Gentry. Environmental Microbiology. Elsevier,

2014. Madigan, M.T., K.S. Bender, D.H. Buckley, W.M. Sattley, and D.A. Stahl. Brock

Biology of Microorganisms, Global Edition. Pearson Education Limited, 2017.


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ENV 622 Soil Biology and Biogeochemical Cycles (3 Cr) Elective, Credits: 3 (3+0) This course provides an advanced understanding of soil biology and biogeochemical cycles. The role of soil biota in ecosystems and production systems is discussed. Topics include carbon, nitrogen, sulfur and phosphorus cycling and underlying processes; methods for assessment of biogeochemical cycling (isotopes, organic matter quality and elemental fluxes); soil organisms and their involvement in processes (metabolism, decomposition, nutrient cycling, weathering and soil structuring), ecology (theory, strategies, community dynamics, interactions, and trophic cascading); basic laboratory techniques in soil biology and biogeochemistry; and human impacts (agriculture, organic farming, global change, ecosystem services and conservation). Recommended Textbooks: Paul, E.A. Soil Microbiology, Ecology and Biochemistry. Academic Press, 2014.

ENV 623 Advanced Reaction Engineering (3 Cr) Elective, Credits: 3 (3+0) This course is to impart and to continue the rigorous study of reaction engineering. In this course, particular emphasis is given to chemical kinetics and transport phenomena, review of elements of reaction kinetics, rate processes in heterogeneous reacting systems, design of fluid-fluid and fluid-solid reactors, scale-up and stability of chemical reactors and residence time analysis of heterogeneous chemical reactors. Recommended Textbooks: Foger, H. Elements of Chemical Reaction Engineering. Prentice Hall, 2016.

ENV 630 Remote-sensing and GIS Applications for Environmental Engineers (3 Cr) Elective, Credits: 3 (3+0) The course covers the principles of remote sensing, general concepts, data acquisition procedures, data analysis and role of remote sensing in terrain investigations for environmental engineering practices. Data collection from airborne and satellite platforms will be emphasized. Photographic and non-photographic sensing methodologies will be covered as well as manual and computer assisted data analysis techniques for site investigations and examination of ground conditions. The course also introduces Geographical/Land Information System (GIS/LIS), which is a computerized system capable of storing, manipulating and using spatial data describing location and significant properties of the earth's surface and its applications for environmental engineers. Topics include data structures and manipulation, topology, and attribute information. Use of spatial data for mapping and spatial analysis to address real world problems. Recommended Textbooks: Campbell, J.B., and R.H. Wynne. Introduction to Remote Sensing. Guilford

Press, 2011. Brimicombe, A. GIS, Environmental Modeling and Engineering, Second Edition.

CRC Press, 2010.


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ENV 641 Water Treatment (3 Cr) Elective, Credits: 3 (3+0) Prerequisite: ENV 605 (Unit Operations in Environmental Engineering) Physical and chemical unit operations for water treatment, emphasizing process combinations for drinking water supply. Application of the principles of chemistry, rate processes, fluid dynamics, and process engineering to define and solve water treatment problems by flocculation, sedimentation, filtration, disinfection, oxidation, aeration, and adsorption. Recommended Textbooks: Hendricks, D.W. Water Treatment Unit Processes: Physical and Chemical. CRC

Press, 2006. Hendricks, D. Fundamentals of Water Treatment Unit Processes: Physical,

Chemical, and Biological. CRC Press, 2016.

ENV 642 Desalination Systems Design (3 Cr) Elective, Credits: 3 (3+0) Prerequisite: ENV 605 (Unit Operations in Environmental Engineering) This subject will survey the state-of-the-art in water purification by desalination. Fundamental thermodynamic and transport processes which govern the creation of fresh water from seawater and brackish ground water will be developed. The technologies of existing desalination systems will be discussed, and factors which limit the performance or the affordability of these systems will be highlighted. Energy efficiency will be a focus. Nanofiltration and emerging technologies for desalination will be considered. Recommended Textbooks: Nikolay, V. Desalination Engineering: Planning and Design, McGraw Hill, 2012.

ENV 643 Domestic Wastewater Treatment (3 Cr) Elective, Credits: 3 (3+0) Prerequisite: ENV 605 (Unit Operations in Environmental Engineering) This course focuses on wastewater characterization, removal of organic matter, removal of nitrogen, biological phosphorus removal, sludge settling properties, settling, aeration and mixing, toxicity, process control, biofilm systems. Recommended Textbooks: Henze, M., M.C.M. van Loosdrecht, G.A. Ekama, and D. Brdjanovic. Biological

Wastewater Treatment. IWA Publishing, 2008.

ENV 644 Industrial Wastewater Treatment (3 Cr) Elective, Credits: 3 (3+0) Prerequisite: ENV 605 (Unit Operations in Environmental Engineering) This course focuses on management of industrial wastewater. Topics include cleaner production, industrial water management, toxicity, physical chemical processes, anaerobic industrial wastewater treatment, and sludge management and treatment. Recommended Textbooks: Pal, P. Industrial Water Treatment Process Technology. Butterworth-

Heinemann, 2017. Patwardhan, A.D. Industrial Wastewater Treatment. Phi Learning Pvt. Ltd,

2017.


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ENV 650 Solid and Hazardous Waste Management (3 Cr) Elective, Credits: 3 (3+0) Introduction to municipal solid waste management and hazardous waste management. The relationship between the properties of wastes, the techniques and hardware used for waste handling and processing and the ultimate disposal (containment) of waste and other residual materials will be emphasized. Remediation of contaminated areas is also covered. The design of systems for the management and disposal of solid and hazardous wastes subject to economic factors, safety, reliability and ethical and social implications will be examined. Recommended Textbooks: Rao, M.N., R. Sultana, S. H. Kota, A. Shah, and N. Davergave. Solid and

Hazardous Waste Management: Science and Engineering. Butterworth-Heinemann. 2016.

ENV 651 Cleaner Production and Waste Valorization (3 Cr) Elective, Credits: 3 (3+0) Prerequisite: ENV 605 (Unit Operations in Environmental Engineering) The course objective is to put the student into the shoes of a plant manager having process responsibility for waste minimization, focusing on recycling and on getting value out of waste. Emphasis is on proven and emerging solutions, especially those associated with heavy metals. Waste minimization generally requires a solid understanding of alternative raw materials and process technologies, in combination with creativity and sensitivity to economics. Recommended Textbooks: Rada, Waste management and valorization: Aternative technologies, Apple

Academic Press, 2016.

ENV 660 Air Quality Measurements and Modeling (3 Cr) Elective, Credits: 3 (3+0) Quantitative overview of the characterization of air pollution problems. Planning and conducting air quality measurements in the atmosphere, indoors, and at the source. Topics include quality control, calibration, and instrument operation for particulate matter, gas and meteorological measurements. Theory and practice of mathematical air quality modeling. Modeling atmospheric chemical transformation processes. Effects of uncertainty in model parameters on predictions. Review of atmospheric diffusion theory and boundary layer meteorology. Dispersion modeling. Combining chemistry and transport. Recommended Textbooks: Hester and Harrison, Air quality in urban environments, Royal Society of

Chemistry, 2009 Griffin, Principles of air quality management, CRC/Taylor & Francis, 2nd

Edition, 2007

ENV 661

Air Pollution Control (3 Cr) Elective, Credits: 3 (3+0) Prerequisite: ENV 605 (Unit Operations in Environmental Engineering) Fundamental engineering principles for preventing or reducing air pollutant emissions. Combustion modifications to prevent pollutant formation. Gas adsorption and absorption processes, including carbon capture and sequestration. Particle filtration processes. Emissions and control of metals and air toxins. Indoor air pollutants and their control. Selected case studies. Recommended Textbooks: Tan, Air pollution and greenhouse gases: From basic concepts to engineering applications for air emission control, Springer-Verlag, 2014.


127

ENV 680 Soil Water Processes in Agroecosystems (3 Cr) Elective, Credits: 3 (3+0) This course discusses physical processes regulating water, energy and solute flows in the soil–plant–atmosphere system. Numerical models are developed and used for simulating climate-driven flows of energy, water and solutes in different types of soil with different types of crops. Practical problems concerning water management are analyzed in relation to land use, crop production, and environmental protection in a changing climate. Recommended Textbooks: Huang, P.M., Y. Li, and M.E. Sumner. Handbook of Soil Sciences: Properties and

Processes, Second Edition. CRC Press, 2011. Kirkham, M.B. Principles of Soil and Plant Water Relations. Academic Press,

2014. Ehlers, W., and M. Goss. Water Dynamics in Plant Production, 2nd Edition. CABI,

2016. Moene, A.f., and J.C. van Dam. Transport in the Atmosphere-Vegetation-Soil

Continuum. Cambridge University Press, 2014. Novak, V. Evapotranspiration in the Soil-Plant-Atmosphere System. Springer

Science & Business Media, 2012.

ENV 681 Soil Salinity and Land Reclamation (3 Cr) Elective, Credits: 3 (3+0) This course covers land degradation; source and accumulation of soluble salts; plant response to salinity; composition of irrigation water; suitability of water for irrigation; salinity management techniques through irrigation; wastewater treatment and re-use for irrigation; salinity control and leaching requirements; reclamation of water logged and salt affected soil through drainage systems; economic and social aspects of land reclamation. Recommended Textbooks: Chhabra, R. Soil Salinity and Water Quality. Routledge, 2017. Hillel, D. Salinity Management for Sustainable Irrigation: Integrating Science,

Environment, and Economics. World Bank Publications, 2000.

ENV 697 Topics in Environmental Engineering (3 Cr) Elective, Credits: 3 (3+0) This course discusses emerging topics in environmental engineering.


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8.2.10. COURSE CATALOG FOR MENG IN ENVIRONMENTAL ENGINEERING

ENV 610 Fundamentals of Environmental Engineering (3 Cr) Compulsory, Credits: 3 (3+0) This course will introduce students to the various areas within environmental engineering and their associated principles. Topics include environmental chemistry, environmental microbiology, environmental decision-making, air quality, air-pollution control, water quality, water and wastewater treatment, desalination, climate change, sustainability and environmental ethics. Recommended Textbooks: Mihelcic, J.R., and J.B., Zimmerman. Environmental Engineering: Fundamentals,

Sustainability, Design. Wiley, 2014.

ENV 630 Remote-sensing and GIS Applications for Environmental Engineers (3 Cr) Compulsory, Credits: 3 (3+0) The course covers the principles of remote sensing, general concepts, data acquisition procedures, data analysis and role of remote sensing in terrain investigations for environmental engineering practices. Data collection from airborne and satellite platforms will be emphasized. Photographic and non-photographic sensing methodologies will be covered as well as manual and computer assisted data analysis techniques for site investigations and examination of ground conditions. The course also introduces Geographical/Land Information System (GIS/LIS), which is a computerized system capable of storing, manipulating and using spatial data describing location and significant properties of the earth's surface and its applications for environmental engineers. Topics include data structures and manipulation, topology, and attribute information. Use of spatial data for mapping and spatial analysis to address real world problems. Recommended Textbooks: Campbell, J.B., and R.H. Wynne. Introduction to Remote Sensing. Guilford

Press, 2011. Brimicombe, A. GIS, Environmental Modeling and Engineering, Second Edition.

CRC Press, 2010.

ENV 631

Computational Methods for Engineers (3 Cr) Elective, Credits: 3 (3+0) Introduction to numerical methods, computational mathematics, and probability and statistics. Development of programming and graphics proficiency with Python (or Matlab) and spreadsheets. Topics include: Taylor-series approximations, numerical errors, condition numbers, operation counts, convergence, and stability, probability distributions, hypothesis testing. Included are numerical methods for solving engineering problems that entail roots of functions, simultaneous linear equations, statistics, regression, interpolation, numerical differentiation and integration, and solution of ordinary and partial differential equations, including an introduction to finite difference methods. Applications are drawn from environmental engineering. Recommended Textbooks: McKinney, Wes. 2017. Python for Data Analysis: Data Wrangling with Pandas,

NumPy, and IPython. 2nd ed. O’Reilly Media. Kinder, Jesse M., and Philip Nelson. 2015. A Student’s Guide to Python for

Physical Modeling. Princeton University Press.


129

ENV 632

Life Cycle Assessment and Costing (3 Cr) Elective, Credits: 3 (3+0) This course explains the four phases of Life Cycle Assessment (LCA): goal and system definition, inventory analysis of emissions and extractions, life cycle impact assessment, and interpretation. Detailed examples of LCA will be provided. Use of LCA software will also be covered. Recommended Textbooks: Crettaz, P., A. Jolliet, J. Olivier, M. Saadé-Sbeih, and S. Shaked. Environmental

Life Cycle Assessment. CRC Press, 2016.

ENV 633

Environmental Risk Assessment (3 Cr) Elective, Credits: 3 (3+0) Quantitative estimation of human health risk posed by the release of a contaminant to the environment. Topics include methods for analyzing emission rate, environmental transport, exposure, and health effects; methods of uncertainty analysis; and the role of risk assessment in environmental regulation and environmental decision-making. Recommended Textbooks: Ofungwu, Statistical Applications for Environmental Analysis and Risk

Assessment, Wiley, 2014 Ricci, Environmental and Health Risk Assessment and Management, Springer,

2010

ENV 634

Environmental and Social Impact Assessment (3 Cr) Elective, Credits: 3 (3+0) This course covers the topics of cumulative impact assessment, greenhouse emission assessments, risk assessments and accident analyses, social impact assessment and environmental justice, environmental and social impact assessment process, and environmental and social management plans (ESMP). Recommended Textbooks: Therivel, R., and G. Wood. Methods of Environmental and Social Impact

Assessment. Routledge, 2017. Eccleston, C.H. Environmental Impact Assessment: A Guide to Best Professional

Practices. 1st ed. CRC Press, 2011.

ENV 640 Water Quality Measurements and Data Analysis (3 Cr) Compulsory, Credits: 3 (3+0) The data analysis topics address practical problems of analysis of manipulation and monitoring datasets in environmental sciences and engineering: hypothesis testing, uncertainty, linear regressions, data of high dimension and time domain and frequency domain analysis of series. Examples are drawn from the fields of environmental engineering and surface and subsurface hydrology. Recommended Textbooks: Quevauviller, P., and C. Thompson. Analytical Methods for Drinking Water:

Advances in Sampling and Analysis. John Wiley & Sons, 2005. Quevauviller, P., O. Thomas, and A. Van Der Beken. Wastewater Quality

Monitoring and Treatment. John Wiley & Sons, 2007.


130

ENV 642 Desalination Systems Design (3 Cr) Elective, Credits: 3 (3+0) This subject will survey the state-of-the-art in water purification by desalination. Fundamental thermodynamic and transport processes which govern the creation of fresh water from seawater and brackish ground water will be developed. The technologies of existing desalination systems will be discussed, and factors which limit the performance or the affordability of these systems will be highlighted. Energy efficiency will be a focus. Nanofiltration and emerging technologies for desalination will be considered. Recommended Textbooks: Nikolay, V. Desalination Engineering: Planning and Design, McGraw Hill, 2012.

ENV 645 Advanced Water and Wastewater treatment (3 Cr) Elective, Credits: 3 (3+0) Theory and design of facilities for physical and chemical treatment of water and wastewater, biological treatment of wastewater, and treatment and disposal of sludge. Study of fundamental principles, rational design considerations, and operational procedures of the unit operations and processes employed in wastewater treatment. Introduces the integration of unit operations and processes into wastewater treatment systems. Recommended Textbooks: Stuetz, R.M., and T. Stephenson. Principles of Water and Wastewater

Treatment Processes. IWA Publishing, 2009.

ENV 650 Solid and Hazardous Waste Management (3 Cr) Compulsory, Credits: 3 (3+0) Introduction to municipal solid waste management and hazardous waste management. The relationship between the properties of wastes, the techniques and hardware used for waste handling and processing and the ultimate disposal (containment) of waste and other residual materials will be emphasized. Remediation of contaminated areas is also covered. The design of systems for the management and disposal of solid and hazardous wastes subject to economic factors, safety, reliability and ethical and social implications will be examined. Recommended Textbooks: Rao, M.N., R. Sultana, S. H. Kota, A. Shah, and N. Davergave. Solid and

Hazardous Waste Management: Science and Engineering. Butterworth-Heinemann. 2016.

ENV 651 Cleaner Production and Waste Valorization (3 Cr) Elective, Credits: 3 (3+0) The course objective is to put the student into the shoes of a plant manager having process responsibility for waste minimization, focusing on recycling and on getting value out of waste. Emphasis is on proven and emerging solutions, especially those associated with heavy metals. Waste minimization generally requires a solid understanding of alternative raw materials and process technologies, in combination with creativity and sensitivity to economics. Recommended Textbooks: Rada, Waste management and valorization: Aternative technologies, Apple

Academic Press, 2016.


131

ENV 660 Air Quality Measurements and Modeling (3 Cr) Compulsory, Credits: 3 (3+0) Quantitative overview of the characterization of air pollution problems. Planning and conducting air quality measurements in the atmosphere, indoors, and at the source. Topics include quality control, calibration, and instrument operation for particulate matter, gas and meteorological measurements. Theory and practice of mathematical air quality modeling. Modeling atmospheric chemical transformation processes. Effects of uncertainty in model parameters on predictions. Review of atmospheric diffusion theory and boundary layer meteorology. Dispersion modeling. Combining chemistry and transport. Recommended Textbooks: Hester and Harrison, Air quality in urban environments, Royal Society of

Chemistry, 2009 Griffin, Principles of air quality management, CRC/Taylor & Francis, 2nd

Edition, 2007

ENV 661

Air Pollution Control (3 Cr) Elective, Credits: 3 (3+0) Fundamental engineering principles for preventing or reducing air pollutant emissions. Combustion modifications to prevent pollutant formation. Gas adsorption and absorption processes, including carbon capture and sequestration. Particle filtration processes. Emissions and control of metals and air toxins. Indoor air pollutants and their control. Selected case studies. Recommended Textbooks: Tan, Air pollution and greenhouse gases: From basic concepts to engineering applications for air emission control, Springer-Verlag, 2014.

ENV 670 Integrated Water Resources Management (3 Cr) Elective, Credits: 3 (3+0) This course covers the following topics about integrated water resources management (IWRM): framework for IWRM, purposes and systems of water management, water governance, integrated urban water systems, water conflicts and treaties, hydrology of water supply and natural systems, demand for water and ecosystem services, water infrastructure and equipment, water infrastructure planning process, models and monitoring in IWRM, water laws and regulations, Economic and decision tools for IWRM, social aspects of water management, water resources and environmental assessment, finance in water management, water security and risk assessment, capacity-building for IWRM, and case studies. Recommended Textbooks: Grigg, N.S. Integrated Water Resource Management: An Interdisciplinary

Approach. Springer, 2016. Borchardt, D., J.J. Bogardi, and R.B. Ibisch. Integrated Water Resources

Management: Concept, Research and Implementation. 1st ed. Springer International Publishing, 2016.


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ENV 686

Renewable Energy Systems (3 Cr) Elective, Credits: 3 (3+0) This course introduces energy systems with emphasis on quantifying costs and designing/optimizing renewable energy systems to convert environmental inputs into useful forms of energy. The course covers solar energy, small scale hydropower, wind, house energy balances, and psychrometric principles as applied to biomass drying. Focuses on the technologies and small-scale system design, assessment of available resources and economic and financial feasibility of state-of-the-art technologies. Recommended Textbooks: Bhatia, Introduction to Advanced Renewable Energy Systems, Woodhead

Publishing Ltd, 2014. Quaschning, Understanding Renewable Energy Systems, Earthscan

Publications, 2005. Formighieri et al., Renewable Energy Systems, Springer-Verlag, 2013 Lund, Renewable energy systems, Academic Press, 2nd Edition, 2014

ENV 687

Concentrated Solar Power and Photovoltaics (3 Cr) Elective, Credits: 3 (3+0) This course presents and assesses the fundamentals of solar energy conversion. It starts with a discussion of the resource and the mechanisms of its propagation through the atmosphere up to the point of conversion. It then discusses the various conversion processes (solar heating/cooling, concentrated thermal power generation and the photovoltaic phenomenon). The state-of-the-art of each of these technologies is then discussed, including their market and economic aspects. Topics include: solar water heating systems, solar space heating and cooling, solar thermal power generation, semiconductors for photovoltaics, power electronics for photovoltaics, grid-connected PV systems, network integration aspects, isolated PV systems and solar energy storage Recommended Textbooks: Jager, Isabella, Smets, van Swaaij, and Zeman, Solar Energy: Fundamentals,

Technology & Systems, Delft University of Technology, 2014.


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8.2.11. COURSE CATALOG FOR PhD IN ENVIRONMENTAL ENGINEERING

ENV 710 Selected Topics in Environmental Sciences (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in environmental sciences.

ENV 715 Selected Topics in Environmental Data Analysis (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in environmental data analysis.

ENV 720 Selected Topics in Water Treatment and Desalination (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in water treatment and desalination.

ENV 721 Selected Topics in Wastewater Treatment (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in wastewater treatment.

ENV 725 Selected Topics in Water Resources Management (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in water resources management.

ENV 726 Selected Topics in Hydrology (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in surface and groundwater hydrology.

ENV 727 Selected Topics in Municipal Hydraulics (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in hydraulics of water supply and wastewater collection networks and hydraulics of water and wastewater treatment plants.

ENV 730 Selected Topics in Computational Fluid Dynamics (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in computational fluid dynamics.

ENV 731 Selected Topics in Environmental Fluid Dynamics (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in environmental fluid dynamics.

ENV 740 Selected Topics in Transport Phenomena (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in transport phenomena.

ENV 750 Selected Topics in Waste Valorization (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in waste valorization.

ENV 760 Selected Topics in Air Quality Engineering (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in air quality modelling, measurement, and control.

ENV 770 Selected Topics in Pollution Control (3 Cr) Elective, Credits: 3 (3+0) This course discusses advanced topics in pollution control.

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