MRC Postdoctoral Fellow

University of Cambridge - MRC Biostatistics Unit

An opportunity has arisen for a talented statistician with an interest in methodology development to join Dr Paul Newcombe's group at the MRC Biostatistics Unit, Cambridge University. In a recent trend "meta-GWAS", comprising tens of thousands of people, have boosted the power of genetic association studies. However, the availability of many correlated genetic variants as well as the logistics of sharing data on such a large scale presents analytical challenges and typically variants are only analysed one-at-a-time. This complicates interpretation and the ability of "fine-mapping" efforts to identify a small set of variants for functional follow-up. The proposed initial project will focus on developing novel statistical methods to extract better inference from the increasing wealth of publicly available meta-GWAS results. Methods will be developed and tested using real life datasets, in particular a collaboration with Dr Zsofia Kote-Jarai's group at the Institute of Cancer Research (Sutton) to explore genetic drivers of prostate cancer risk. This post offers an exciting opportunity to work alongside and build collaborative relationships with world-leading researchers, while addressing cutting-edge research questions in rich and real datasets. There will be opportunities to lead projects as well as publish papers as a first author in high quality peer-reviewed journals.

Dr Paul Newcombe's group was formed with the broad aim of developing statistical methods for the analysis of genomic data to improve understanding of common diseases and disease traits in humans. In addition to the proposed project above, there is scope for work on a range of other topics and the post holder will be encouraged to develop other research interests relevant to the goals of the group. For more information about Dr Paul Newcombe's research see: The MRC Biostatistics Unit (, located in Cambridge and now part of the University, undertakes research on statistical methods and their application to the design, analysis and interpretation of biomedical studies, to advance understanding of the cause, natural history and treatment of disease, and to evaluate public health strategies. It is the largest grouping of statisticians in Europe and offers an excellent environment in which to develop a career.

By the time they take up the appointment, the successful applicant will have a PhD (or equivalent) in a strongly quantitative subject, ideally statistics. Experience of Bayesian methodology and, in particular, approaches to variable selection/sparse regression would be desirable. An understanding of genomics would be advantageous but not essential; full training will be given on the basic concepts necessary to the post. Most important are an inquisitive mind and the desire to develop and apply statistical methodology to questions of substantive biological importance and disease relevance. The successful applicant will be supported in their career development with a range of formal courses and on-the-job training.

This post is a fixed-term 3 year training programme.

To apply online for this vacancy and to view further information about the role, please visit: This will take you to the role on the University’s Job Opportunities pages. There you will need to click on the 'Apply online' button and register an account with the University's Web Recruitment System (if you have not already) and log in before completing the online application form.

Please ensure that you upload a covering letter and CV in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

The closing date for applications is Wednesday 25th October 2017. Interviews are likely to be held on Monday 6th November 2017.

For an informal discussion about this post please contact

Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.

Please quote reference SL13272 on your application and in any correspondence about this vacancy.

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