Potential Theory of Regenerative Compositions at Queen Mary University of London on FindAPhD.com

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Prof A Gnedin 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 2020 for self-funded students.

This project will be supervised by Prof. Alexander Gnedin.

Composition structure is a consistent sequence of random ordered partitions, one for each integer n. There is a very precise correspondence between the increasing Lévy processes (subordinators) and composition structures with the property of regeneration, which allows one to model composition as path of a certain Markov chain. The aim of the project is to study how by the virtue of this correspondence the potential theory of subordinators translates in combinatorial terms, in particular as properties of the Green function of the Markov chain.

The application procedure is described on the School website. For further inquiries please contact Prof. Alexander Gnedin 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.