Applied Mathematics and Computational Science M.Sc. Program

It is the responsibility of students to plan their graduate program in consultation with their academic advisor. Students are required to meet all deadlines. Students should be aware that most core courses are offered only once per year.

The Master of Science (M.Sc.) degree is awarded upon successful completion of a minimum of 36 credit hours. A minimum GPA of 3.0 must be achieved to graduate. Individual courses require a minimum of a B- for course credit. Students are expected to complete the M.Sc. degree in four semesters, (including Summer, thus, for Fall admissions, students should complete the M.Sc. degree by the end of their second Fall semester). Satisfactory participation in every KAUST summer session is mandatory; this participation may take the form of course enrollment, internships, or directed research.

 

The M.Sc. Requirements

  • Core courses (6-15 credits)
  • Elective courses (9-18 credits)
  • Research/capstone experience (12 credits)
  • Graduate seminar 398 (non-credit) – all students are required to register and receive a satisfactory grade for the first two semesters
  • Completion of one Winter Enrichment Program (WEP)

Winter Enrichment Program

Students are required to satisfactorily complete at least one full winter enrichment program (WEP).

Core Courses (6-15 credits)

The core courses are designed to provide students with the background needed to establish a solid foundation in the program area. Students should be aware that most core courses are offered only once per year.

Applied Mathematics (AM) Track (15 credits)

AMCS 231Applied Partial Differential Equations I

3

AMCS 235Real Analysis

3

AMCS 251Numerical Linear Algebra

3

AMCS 252Numerical Analysis of Differential Equations

3

AMCS 241/STAT 250Stochastic Processes

3

Or

STAT 220Probability and Statistics

3

 

Computational Science and Engineering (CSE) Track (6 credits)

Students must fulfil at least two of the four core courses below:

AMCS 231Applied Partial Differential Equations I

3

AMCS 251Numerical Linear Algebra

3

AMCS 252Numerical Analysis of Differential Equations

3

AMCS 241/STAT 250Stochastic Processes

3

Or

STAT 220Probability and Statistics

3

 

Data-Science-DS-Track-12-credits

Students must fulfil the four core courses below:

AMCS 211Numerical Optimization

3

AMCS 215Mathematical Foundations of Machine Learning

3

AMCS 251Numerical Linear Algebra

3

AMCS 241/STAT 250Stochastic Processes

3

Or

STAT 220Probability and Statistics

3

Core courses can be replaced by 300-level courses in the same research area if the student has previously taken a similar course. This substitution must be approved both by the student advisor and by the AMCS curriculum committee. 

Elective Courses (9-18 credits)

The elective courses (which exclude research, internship credits, and IED courses) are designed to allow students to tailor their educational experience to meet individual research and educational objectives with the permission of the academic advisor.

Applied Mathematics (AM) Track

9 credits of elective courses not necessarily within the AMCS program. Some credits may be taken outside the AMCS program subject to the approval of the academic advisor.

Computational Science and Engineering (CSE) Track

Students in the CSE track must take an additional eighteen credits of coursework made up of:

•            6 credits of computer science courses

•            6 credits in applications of modeling. Eligible application courses include AMCS 332 (mathematical modelling). At least one of the modeling courses should be from outside AMCS. In case both courses are from outside AMCS, it is recommended that they be drawn from the same track. The choice of modeling courses must be approved both by the advisor and by the program chair.

•            6 credits from AMCS courses

Data Science Track DS

Students in the DS track must take an additional twelve credits of coursework. The choice of courses must include at least 3 credits in AMCS. The choice of elective courses must include at least 6 credits of courses relevant to data science, according to a course list maintained by the AMCS curriculum committee.  

M.Sc. Thesis

The thesis defense committee, which must be approved by the dean, must consist of at least three members and typically includes no more than four members. At least two of the required members must be KAUST faculty. The chair plus one additional faculty member must be affiliated with the student’s program. This membership can be summarized as:

Member Role Program Status
1 Chair Within program
2 Faculty Within program
3 Faculty or approved research scientist Outside program
4 Additional faculty or research scientist Inside or outside KAUST

Notes:

  • Members 1-3 are required, member 4 is optional
  • Co-chairs may serve as member 2, 3, or 4, but may not be a research scientist
  • Members 2 and 3 must use primary affiliation only
  • Adjunct professors and professors emeriti may retain their roles on current committees, but may not serve as chair on any new committees
  • Professors of practice and research professors may serve as members 2, 3 or 4 depending upon their affiliation with the student’s program, they may also serve as co-chairs
  • Visiting professors may serve as member 4
  • The affiliation status, within program/outside program refers to primary affiliation. 

View a list of faculty and their affiliations here.

M.Sc. Non-Thesis

Students wishing to pursue the non-thesis option must complete a total of 12 capstone credits, with a maximum of 6 credits of directed research (299).

Students must complete the remaining credits through one or a combination of the options listed below:

  • Broadening experience courses: courses that broaden a student’s M.Sc. experience. The choice of these courses must be approved by the program chair
  • Internship: research-based summer internship (295) – students are only allowed to take one internship
  • Ph.D. courses: courses numbered at the 300 level