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Parametric Complexity Reduction in Finance


   School of Mathematical Sciences

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

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:

  • Fourier pricing;
  • Monte Carlo simulation;
  • Model order reduction techniques;

• Applications to

  • Pricing;
  • Hedging;
  • Model calibration;
  • Risk management;
  • Modelling financial asset evolution;

• 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.

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