Numerical Methods for High-Dimensional Problems in Finance at Queen Mary University of London on FindAPhD.com

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Dr K Glau Applications accepted all year round Competition Funded PhD Project (Students Worldwide)

London United Kingdom Applied Mathematics Probability

About the Project

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

The research focuses on:

• Development of new computational methods for finance:

-Function approximation;
-Dimensionality Reduction;
-Complexity Reduction;

• Creation of synthetic data sets;

• Applications to pricing and risk management;

• The analysis of the reliability and efficiency of the new methods.

Requirements:

• Strong background in mathematics, particularly in numerical mathematics;

• Very good programming skills (Matlab/Python/C++) desired;

• Prior knowledge of the field of computational finance is useful, but not required.

The application procedure is described on the School website. For further inquiries please contact Dr Kathrin Glau at [Email Address Removed]. 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.

Funding Notes

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.
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.