[DMANET] PhD studentship at the University of Glasgow in Algorithms for Spatial Statistics (closing date 21st June)

PhD Studentship: Developing novel ways to represent spatial patterns in disease risk

Background:

Disease mapping is a field of statistics which is interested in identifying the spatial pattern in disease risk across a region. The goal of such analysis is typically to identify high (and low) risk areas and subsequently to investigate the factors which lead to these differences in risk. Understanding the extent of the health inequalities which exist within a region allow public health interventions to be made in the appropriate locations.

This analysis is traditionally carried out via generalised linear mixed models, where the spatial structure of the data is accounted for via the correlation structure of the random effects using what are known as conditional autoregressive (CAR) models. The basic premise of a CAR model is that areas which are closer together geographically are likely to have more in common than those which are further apart, in other words we assume that disease risk is likely to evolve smoothly across our region.

However, this assumption does not always hold in practice, with many cities having more localised patterns of inequality. Here in Glasgow, the suburb of Bearsden to the north of the city has an average life expectancy of 82.3, compared to just 70 in neighbouring Drumchapel. Therefore, the goal of this project will be to identify different ways of representing these spatial patterns within our models to provide more accurate estimation of the disease inequality.

In addition to more traditional statistical approaches, the project will also make use of novel combinatorial algorithms to identify optimal spatial patterns; the relative emphasis on the design of algorithmic techniques and their use to carry out statistical analysis will depend on the background and interests of the student. The project is therefore suitable for a student looking to carry out research in any of statistics, combinatorics, or theoretical algorithm design.


Project Details

The project is jointly supervised by Dr Kitty Meeks (School of Computing Science) and Dr Craig Anderson (School of Mathematics and Statistics) at the University of Glasgow. The supervisors already work together on existing projects and are working on a grant proposal related to the topic of this PhD project (the funding of this PhD scholarship is not dependent on the outcome of the grant proposal).

A successful candidate would receive an fully-funded EPSRC scholarship for 4 years, starting in October 2020. This scholarship would include the payment of all university tuition fees in addition to an annual stipend in line with the UKRI doctoral stipend level, which is £15,285 for 2020/21. Students will be required to meet the EPSRC eligibility requirements, though in exceptional cases funding may be available for international students. https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility/


We would expect the candidate to have (or be on track to achieve) a First Class Honours degree in a relevant area of mathematical sciences (eg Statistics, Mathematics, Computer Science, Data Science), and to have some experience of programming.


How to apply

Please send your application to craig.anderson@glasgow.ac.uk<mailto:craig.anderson@glasgow.ac.uk> by 5pm on Sunday 21st June. Your application should include the following:


* A cover letter of at most two pages explaining why you are interested in the project and what skills and ideas you believe you would contribute to the project
* An up to date curriculum vitae (CV)
* Evidence of a first class degree in a relevant subject, or evidence that you expect to obtain such a degree prior to September 2020.

Informal inquiries to either supervisor (craig.anderson@glasgow.ac.uk<mailto:craig.anderson@glasgow.ac.uk> or kitty.meeks@glasgow.ac.uk<mailto:kitty.meeks@glasgow.ac.uk>) are welcome.


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Kitty Meeks

Royal Society of Edinburgh Research Fellow

School of Computing Science, University of Glasgow

http://www.dcs.gla.ac.uk/~kitty/

Direct line: +44 (0)141 330 1631


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