PhD Studentship in Statistics, "Bayesian inference for big genomic data"
In the past decade the cost of DNA sequencing has decreased dramatically. It is becoming routine to sequence large numbers of whole genomes in a wide range of species. Such data contains extremely detailed information about the evolution of the species under consideration. The implications of this can be hugely significant in many applications. The Modernising Medical Microbiology (MMM) project at the University of Oxford uses this technology for investigating the evolution and epidemiology of human pathogens to answer questions that were previously unaccessible. Successes of this project so far include tracking the transmission of pathogens and in predicting their resistance to antibiotics - the MMM website (www.modmedmicro.ac.uk) has further details of these applications and of other work. This PhD is part of a collaboration between Mathematics and Statistics at the University of Reading and the MMM project, seeking to continue to make scientific breakthroughs through statistical analysis of whole genome sequence (WGS) data.
The student will conduct research in the area of Monte Carlo simulation based methods of statistical inference for intractable problems, motivated by applications in the analysis of WGS data. You will be based in the Department of Mathematics and Statistics at the University of Reading for the majority of the studentship, but in addition will spend time at the John Radcliffe Hospital in Oxford working with experts who have domain-specific expertise in pathogen genomics. This project offers the chance to develop innovative methods for analysing WGS and to use them to answer applied research questions.
The project offers the scope for personal development to students who are interested in methodological or applied statistics, or statistical genomics. Further details of the project may be obtained from Dr. Richard Everitt ([log in to unmask]).
Student eligibility
You should have at least a good 2.1 degree (predicted or actual) and a background in mathematical statistics, ideally with an interest in Bayesian statistics, Monte Carlo methods or the analysis of genomic data. Familiarity with a programming language such python, Matlab, R or C++ is also desirable. The funding is available for UK or EU applicants only.
Enquiries
For informal enquiries contact Dr. Richard Everitt ([log in to unmask]).
How to apply
Apply online by 18th April 2014. For details on how to apply, see www.reading.ac.uk/graduateschool/prospectivestudents/gs-how-to-apply.aspx
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