PhD Studentship: Bayesian Inference and Approximations of High-Dimensional Network Models

University of Sussex - Department of Mathematics

A fully-funded 3-year PhD position is available in the Department of Mathematics in the School of Mathematical and Physical Sciences at the University of Sussex. The PhD studentship is part of a research project funded by the Leverhulme Trust which involves a  Postdoc and a Teaching Fellow. The start date of the project is 1st of September 2018. It involves a team led by Prof Istvan Kiss with local (Dr Masoumeh Dashti and Dr Luc Berthouze) and international collaborators (Prof Andrew Stuart, Caltech).

The use of networks to model complex systems has revolutionised the way in which brains, epidemics, social interactions and more generally the flow of information are modelled. However, many of the resulting mathematical models suffer from high model dimensionality and therefore limited analytical tractability and inaccuracies due to simplifying assumptions or approximations. The aim of this project is to develop a new modelling paradigm which relies on the specification of a new class of parametric models that are flexible enough to handle networks currently out of reach of state-of-the-art models. The inference of the parameters is formulated as Bayesian inverse problems, which in turn makes it possible to rigorously quantify the uncertainty introduced by simplifying assumptions and incomplete network data. This research will seek to harness a novel combination of techniques from stochastic analysis, partial differential equations (PDEs) and uncertainty quantification and could prove a step change in the ability of network science to deal with real-world applications.

The PhD student will be expected to play a key role in developing rigorous and efficient stochastic simulation techniques on networks as well as being responsible for developing a statistically sound network-coefficient classification. The student will also be expected to contribute to the theory and computational techniques underpinning the Bayesian inference framework to be implemented within the project. The project will also provide opportunities to get familiar with elements of Uncertainty Quantification and Bayesian Inverse problems.

Type of award

Postgraduate Research

Amount

£14,777 (2018-2019) per year tax-free bursary plus the waiver of UK/EU fees each year for 3 years. Full-time study.

Eligibility

Applicants should hold, or expect to hold, a UK undergraduate/masters degree, or equivalent, in mathematics or a related subject. Due to funding restrictions, the studentship is open to UK and EU resident students only.

Deadline

21st of May 2018 23:59 (GMT)

How to apply

Online applications at: http://www.sussex.ac.uk/study/phd/apply.

State in the Funding section of the application form that you are applying for the "PhD Studentship in Bayesian Inference and Approximations of High-Dimensional Network Models”

The award includes appropriate funds for computing, books, open access publishing and the attendance to conferences and workshops.

For informal enquiries, please contact Prof Istvan Kiss (i.z.kiss@sussex.ac.uk) and Dr Masoumeh Dashti (M.Dashti@sussex.ac.uk) by email.

For practical questions about the application process and/or eligibility for funding, please contact: mpsresearchsupport@sussex.ac.uk

Application deadline: 21st of May 2018 23:59 (GMT)

Start date: 1st of September 2018

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Type / Role:

PhD

Location(s):

South East England