The MRC Biostatistics Unit (http://www.mrc-bsu.cam.ac.uk), located in Cambridge, 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. An opportunity has arisen for a talented statistician with an interest in methodology development to work with Prof Sylvia Richardson and Dr Lorenz Wernisch as part of the `Statistical-genomics' working group. The broad aims of the group are to develop statistical methods for the analysis of genomic data to improve understanding of common diseases and disease traits in humans.
In collaboration with Prof David Menon (Dep of Medicine, Cambridge University) the statistician will contribute to the CENTER-TBI project with the aim of improving the outcome prediction after a traumatic brain injury (TBI) based on phenotypic and genetic data. The prognosis of the functional outcome of TBI is a complex task since it is based on data from different sources: fMRI and CT imaging, biomarker assays, ICU (intensive care unit) monitoring data. A challenging aspect of the project is the need to interface the statistical models with existing machine learning tools applied to the image data (CNNs for image segmentation and parcellation) and time series analyses of ICU data. The post holder will work closely with experts in image and time series analysis. This post offers an exciting opportunity to work alongside and build collaborative relationships with a range of 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.
By the time they take up the appointment, the successful applicant will have a PhD in a strongly quantitative subject, ideally statistics. Experience of Bayesian methodology and some familiarity with machine learning approaches would be desirable. An understanding of genomics, image analysis or time-series analysis would be advantageous but not essential; full training will be given on the basic concepts necessary to the project 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.
For an informal discussion about this post please contact firstname.lastname@example.org
Fixed-term: The funds for this post are available until 31 March 2020 in the first instance.
To apply online for this vacancy, please click on the 'Apply' button below. This will route you to the University's Web Recruitment System, where you will need to register an account (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.
Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.
The closing date for applications is the 5th September 2017 with interviews likely to be held on 18th September 2017.
Please quote reference SL12933 on your application and in any correspondence about this vacancy.
The University values diversity and is committed to equality of opportunity.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.