The Faculty of Science offers a program in Scientific Computing leading to a Doctor of Philosophy (Ph.D.). Full-time and part-time options are available.

To be considered for admission,

1. Applicants shall normally hold an M.Sc. degree in Scientific Computing, or equivalent, from a university of recognized standing, or

2. Applicants shall normally hold an M.Eng. or an M.Sc. degree in an appropriate discipline from a university of recognized standing, or

3. Students currently registered in the Master of Science (Scientific Computing), after a minimum of 12 months of successful performance in their program of studies, may be recommended for transfer into the Ph.D. program, provided that the students can demonstrate, to the satisfaction of the Board of Study, an ability to pursue research at the doctoral level. This transfer normally occurs no later than the fifth semester of the student’s M.Sc. program.

4. In exceptional cases, applicants holding a B.Sc. (Honours or equivalent) degree in an appropriate discipline that included completion of a thesis or dissertation from a university of recognized standing, may be considered for direct admission into the PhD program.

The supervisory committee, in consultation with the Board of Study, will determine the minimum course requirements and the program of study for each Ph.D. student in Scientific Computing. Substitutions for courses on the list of core scientific computing courses are possible. Given the interdisciplinary nature of the program, the following guidelines would be followed:

1. Students who have completed an M.Sc. degree in Scientific Computing or equivalent will be required to complete two courses (6 credit hours) chosen from the list of core courses or two courses (6 credit hours) from the application area as appropriate.

2. Students who have completed an M.Eng. or a disciplinary M.Sc. degree will be required to complete four courses (12 credit hours). Normally, three (9 credit hours) of these courses would be chosen from the list of core courses to ensure sufficient training in scientific computing.

3. Students who transfer to the Ph.D. program from the Master of Science (Scientific Computing) program are required to complete six courses (18 credit hours) in total. Normally three to four of these courses would be from the list of core courses.

4. Students holding a B.Sc (honours or equivalent) degree who are directly admitted into the program will be required to complete six courses (18 credit hours). Normally three to four of these courses would be from the list of core courses.

5. Students are required to pass a single Comprehensive Examination as prescribed under General Regulations, Comprehensive Examinations, Ph.D. Comprehensive Examination. This shall be an oral exam, and may include the presentation of a written research proposal.

6. Upon completion of the work for the thesis, each student is required to present a seminar suitable for the interdisciplinary audience of Scientific Computing program students.

The submission of an acceptable thesis is required. The thesis is to contain an original scholarly contribution which must be submitted to the School of Graduate Studies for final examination. The thesis must be written in a format according to procedures outlined in Guidelines for Theses and Reports by the School of Graduate Studies at www.mun.ca/sgs/go/guid_policies/theses.php.

Scientific Computing

• 6910 Matrix Computations and Applications or COMP 6732 Matrix Computations (credit may be obtained for only one CMSC 6910 and COMP 6732)
• 6920 Scientific Programming
• 6930 Algorithms for Distributed and Shared Memory Computers
• 6950 Computer Based Tools and Applications (credit may be obtained for only one of CMSC 6950 and the former CMSC 6940)
• 7000-7009 Selected Topics in Scientific Computing

Computer Science

• 6906 Topics In Numerical Methods (credit may be obtained for only one of COMP 6906 and COMP 6731)

Mathematics

• 6201 Numerical Methods for Time Dependent Partial Differential Equations
• 6202 Nonlinear and Linear Optimization
• 6204 Iterate Methods In Numerical Linear Algebra
• 6210 Numerical Solutions of Differential Equations