About the Project
This project will be supervised by Dr. Kathrin Glau.
Development of new computational tools for high-dimensional problems. This will involve dierent 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.
- 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 Scientic 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 [email protected].
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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