The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing in September 2021.
This project will be supervised by Dr. Kathrin Glau.
Development of new computational tools for high-dimensional problems. This will involve different 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.
This project is eligible for full funding, including support for 3.5 years’ study, additional funds for conference and research visits and funding for relevant IT needs. Applicants interested in the full funding will have to participate in a highly competitive selection process.
For September 2021 entry: Funding may be available through QMUL Principal’s Postgraduate Research Studentships, School of Mathematical Sciences Studentships, and EPSRC DTP, in competition with all other PhD applications.
Studentships will cover tuition fees, and a stipend at standard rates for 3-3.5 years.
We welcome applications for self-funded applicants year-round, for a January, April or September start.
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 at [email protected].
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 Scientific 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/
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.