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Our advanced Financial and Computational Mathematics program prepares graduates with essential expertise for thriving in quantitative finance careers. Contemporary financial technology relies on complex computational methods for modeling asset behaviors, market dynamics, and derivative valuations. This curriculum delivers comprehensive training in computational finance and machine learning applications, featuring a collaborative research component with potential industry collaborations. Alumni have secured positions in risk assessment (Acadia, China Reinsurance Corporation), financial regulation (FinTru, KBRA), and investment management (Fidelity), while others have pursued SFI-funded doctoral research in financial machine learning. The program equips students for diverse opportunities across the financial industry, with special emphasis on quantitative analysis, risk evaluation, and investment banking careers.
Candidates must have obtained at least a 2.2 degree or equivalent in the mathematical sciences or another quantitative subject.
Candidates who have obtained at least a 2.2 honours degree in Engineering or Physics will be considered and should be able to demonstrate to the Course Coordinator some prior experience of probability and statistics, linear algebra, multivariate calculus, ordinary differential equations, and programming.
All candidates must be ultimately approved by the Course Coordinator.
Duolingo English Test: DET 120 (with a minimum score of 110 in each individual section)
IELTS: 6.5 Overall; 6.0 Individual Skills (academic version)
TOEFL iBT: 90 Overall; Listening 20; Reading 19; Speaking 21; Writing 20
Cambridge Examinations:
C2 Proficiency
C1 Advanced
B2 First
176 overall with no less than 169 in each element of the test
Pearson PTE: Minimum score of 63 with no section score below 59