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

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing either in September 2019 for students seeking funding, or at any point in the academic year for self-funded students. The deadline for funded applications was 14 January 2019 (if you wish to be considered for the Alan Turing Institute studentship) or 31 January 2019 for all other funded studentships.

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: http://www.maths.qmul.ac.uk/~kglau/

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

The project can be undertaken as a self-funded project, either through your own funds or through a body external to Queen Mary University of London. Self-funded applications are accepted year-round.

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. Further information is available here. We strongly encourage applications from women as they are underrepresented within the School.

We particularly welcome applicants through the China Scholarship Council Scheme.

Related Subjects

How good is research at Queen Mary University of London in Mathematical Sciences?

FTE Category A staff submitted: 34.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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