PhD projects in Financial Mathematics at Queen Mary University of London

The School of Mathematical Sciences of the Queen Mary University of London will soon be inviting applications for PhD studies in deep learning, numerical methods and modelling with stochastic processes for Finance.

We are seeking highly motivated and skilled students with a strong background in mathematical theory and experience in programming who are interested in numerical analysis, deep learning, stochastic processes and their application to financial mathematics. The project will be supervised by the research team in mathematical finance of Dr Kathrin Glau and Dr Linus Wunderlich in the Research Group Probability and Applications

Core of the project is the combination of Chebyshev interpolation, deep learning, low rank tensor approximation, and modelling with stochastic processes with jumps to solve the dynamical systems arising in risk management in three different guises: as partial (integro) differential equations, as dynamic programming problem and as stochastic differential equations. Topics for the project start in 2022 will be advertised soon, a good impression of the topics can be obtained here:

Some clearly stated initial projects will allow the PhD student to immediately contribute to advancing the research in the field while learning the methodologies and applications we consider. The project is also wide enough so that the PhD student will be able to develop an own focus, contributing to the theoretical analysis, the implementation, the application or to an industrial collaboration. PhD students are enrolled at Queen Mary University of London and take part in the London Graduate School in Mathematical Finance - a consortium of the financial mathematics groups including Birkbeck College, Brunel University, Cass Business School, Imperial College, King's College, LSE and UCL.
The application deadline for Queen Mary University of London's funded PhD studentships is 30 January 2022. For further information see

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