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| Program start date | Application deadline |
| 2025-09-08 | - |
Program Overview
Financial & Computational Mathematics
Course Fact File
| Code | MSCFCM |
|---|---|
| Duration | 1 Year |
| Teaching Mode | Full-time |
| Qualifications | MSc |
| NFQ Level | Level 9 |
| Closing Date | Rolling deadline. Open until all places have been filled. Early application is advised. |
| Non-EU Closing Date | Open until all places have been filled or no later than 15 June. Early application is advised. |
| Start Date | 8 September 2025 |
Course Outline
Our postgraduate Financial and Computational Mathematics programme equips graduates with the skills necessary to pursue a successful career in quantitative finance. Modern financial technology is based upon sophisticated computational techniques for the modelling of asset and market movements, and the valuation of financial derivatives. This course provides a solid grounding in computational finance along with machine learning techniques and includes a team-based research project with opportunities to work with our industry partners.
Career Opportunities
Past graduates of this programme have been recruited to risk management (Acadia, China Reinsurance Corporation), regulatory (FinTru, KBRA), and wealth management/investment roles (Fidelity). They have also progressed to SFI-funded study at PhD level in the application of machine learning to finance. This programme will prepare you for a broad spectrum of roles in the financial sector and particularly in quantitative finance, risk analysis, and investment banking.
What our students say:
The three areas that have most interested me are mathematics, finance and programming/software development. Thanks to what I had learned and developed through this MSc programme, I was able to demonstrate a good understanding of the methodologies and risk management framework on which my current company (Acadia) builds upon with their analytics software, and I was able to get up to speed with our full tech stack at a quick pace...
Nathaniel Volfango, Graduate 2021 - Quantitative Developer at Acadia
I absolutely love maths and its logical problem-solving element. I enjoyed programming from my undergrad, and so this course was the natural way to go to learn more about a field that incorporates both of these subjects in a practical financial context. This programme has helped me learn a lot more and develop skills that I know are in high demand in the finance sector...
Arinjoy Bhanja, Graduate 2022 – Credit Risk Analyst at KBRA
Programme Structure
Part I (60 credits)
- Core Modules (45 credits)
- MF6010 Probability Theory in Finance (10 credits)
- MF6011 Derivatives, Securities, and Option Pricing (5 credits)
- MF6012 Computational Finance I (5 credits)
- MF6013 Computational Finance II (5 credits)
- MF6014 Topics in Financial Mathematics (5 credits)
- MF6015 Continuous-Time Financial Models (5 credits)
- AM6004 Numerical Methods and Applications (5 credits)
- CS6322 Optimisation (5 credits)
- Elective Modules (Choose 15 credits)
- AM4062 Applied Stochastic Differential Equations (5 credits)
- AM6007 Scientific Computing with Numerical Examples (5 credits)
- AM6019 Partial Differential Equations (5 credits)
- ST4400 Data Analysis II (5 credits)
- ST6040 Machine Learning and Statistical Analytics I (5 credits)
- ST6041 Machine Learning and Statistical Analytics II (5 credits)
- CS6503 Introduction to Relational Databases (5 credits)
Part II (30 credits)
- MF6016 Dissertation in Financial and Computational Mathematics (30 credits)
Course Practicalities
In Semesters 1 and 2 you can expect to attend an average of 12 hours of lectures and 6-8 hours of tutorials and lab sessions per week, which will be spread evenly throughout the working day. The remainder of your time will be spent in independent study, exercises, and assignments.
Semester 3 will be devoted to a substantial research project leading to a dissertation and a workshop where you will present your findings to your peers and the course team.
Computer labs are provided on campus with all relevant software packages, though students are also encouraged to have access to a laptop of their own.
Teaching at UCC is research-led, and the course is delivered by faculty staff from the School of Mathematical Sciences, including mathematicians, statisticians, and computer scientists who are internationally recognised for their research, ensuring that you will have access to up-to-date knowledge in the field. Relevance to current industry practice is ensured through our industry partners.
Why Choose This Course
Employers in the banking and investment sector require graduates with an understanding of the relevant mathematical concepts as well as the practical and computational skills associated with applying them. This course provides both, and is an opportunity for graduates, especially those with a background in the mathematical sciences, physics, or engineering, to develop high-level skills in computational finance, machine learning, and mathematical finance.
UCC itself enjoys proximity to financial employers in Ireland (for example the International Finances Services Centre [IFSC] in Dublin) and in other European financial centres, including London.
Connected Curriculum
Our learning approach reflects our commitment to the Connected Curriculum where we emphasise the connection between students, learning, research and leadership through our vision for a Connected University. Our staff are at the forefront of this integrative approach to learning and will support you in making meaningful connections within and between topics such as mathematics, finance, and technology.
Available Scholarships
We support our postgraduate community by offering scholarships and bursaries to prospective and current students. Please see the SEFS Scholarships and Funding PG page for more information.
Skills and Careers Information
Graduates will be prepared for quantitative roles in the financial services sector and particularly in investment banking, including roles in financial engineering, quantitative finance, investment analysis, and fund management. The programme also serves as a route to further study in this area.
Requirements
- 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.
- Candidates, for whom English is not their primary language, should possess an IELTS of 6.5 (or TOEFL equivalent) with no less than 6.0 in each individual category.
- All candidates must be ultimately approved by the Course Coordinator.
Fees and Costs
Postgraduate EU and International Fees 2025/2026
See our Postgraduate EU and Non-EU (International) Fee Schedule for the latest information.
Deposits
If your course requires a deposit, that figure will be deducted from your second-semester fee payment in January.
Fee payment
Fees are payable in two equal instalments. First payment is at registration and the balance usually by the end of January.
How can I pay?
See different options on our How Do I Pay My Fees? page.
