Research Associate in Precision Medicine and Inference for Complex Outcomes

University of Cambridge

An opportunity has arisen for a talented statistician researcher at the MRC Biostatistics Unit (BSU), Cambridge University, within the Precision Medicine and Inference for Complex Outcomes theme.

The successful applicant will have the opportunity to contribute to the work of the multi-site HDRUK Implementation project: Measuring & Understanding Multimorbidity using Routine Data in the UK (MUrMuR-UK) project, PI Prof Colin McCowan (St Andrews) and involves HDRUK groups in Scotland, Wales/NI, Midlands, Cambridge, Oxford and London. The project is led by Prof Sylvia Richardson for the Cambridge site.

Multimorbidity is when people suffer from more than one long-term illness. It is increasingly common as people live longer. It is important because: individual illnesses have knock-on effects on others, it is more complex managing multiple than single illnesses, and multimorbid patients are heavy users of medications and health services. Electronic health records (EHRs) are a good source of information on multimorbidity because they include information on the same patient over many years. As part of the MUrMuR-UK project, a network of analytics hubs across the sites will be implementing or extending relevant statistical and machine learning methods and comparing methods to answer key research questions about variation in MM by geography and socio-demographic variables, clusters of disease and disease trajectories and their health burden. The postholder will be expected to network with the other analytic hubs and will also benefit from liaising with another research project on multimorbidity currently underway between the BSU and Birmingham University.

We are seeking an ambitious and motivated individual to join the MUrMuR-UK analytic team in Cambridge. The successful candidate will have a PhD in a strongly quantitative discipline, ideally statistics. Past experience with EHRs and/or other "big data" sources would be advantageous, but not essential; training will be given on the basic concepts necessary to the post. A desire to address questions of substantive biomedical and societal importance is essential. Good communication skills and an enthusiasm for collaborating with others (including non-statisticians) are also essential. Strong programming ability would be desirable. The successful applicant will be supported in their career development with a range of formal courses and on-the-job training.

Fixed-term: The funds for this post are available for 18 months in the first instance.

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Closing date for applications is 17/01/2021 with interviews date to be confirmed.

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Please quote reference SL25189 on your application and in any correspondence about this vacancy.

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