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Computational Methods for High-Dimensional Problems in Finance using PDE Methods and Deep Learning

School of Mathematical Sciences

About the Project

The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing in September 2020 for self-funded students.

This project will be supervised by Dr. Kathrin Glau.

Development of new computational tools for high-dimensional problems. This will involve di erent techniques from numerical analysis and statistical learning. The tools will be developed, implemented and extensively tested numerically and theoretically. We will particularly build on PDE methods and deep learning.

Further characteristics:
- high practical relevance of the topic,
- close collaboration with nancial industry is intended,
- interdisciplinary topic involving mathematical nance, numerical analysis, machine learning.

Research group: Two recent publications within the current PhD project with Christian Potz:
- A new approach for American option pricing: The Dynamic Chebyshev method, K. Glau, M. Mahlstedt and C. Potz (2018), accepted for publication in the SIAM Journal of Scienti c Computing

- The Chebyshev method for the implied volatility, K. Glau, P. Herold, D. B. Madan and C. Potz (2018), accepted for publication in the Journal of Computational Finance

Further information:

Requirements: Strong background in mathematics, strong background in numerics, very good programming skills (Matlab/Python/C++) desired. Prior knowledge of the eld of computational nance would be useful, but not required.

The application procedure is described on the School website. For further inquiries please contact Dr. Kathrin Glau .

Funding Notes

This project can be undertaken as a self-funded project. Self-funded applications are accepted year-round for a January, April or September start.

The School of Mathematical Sciences is committed to the equality of opportunities and to advancing women’s careers. As holders of a Bronze Athena SWAN award we offer family friendly benefits and support part-time study.

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