Research Associate (Postdoc) in Statistical Physics/Evolution/Computational Modelling of Cancer

University of Cambridge - Department of Oncology

The Blundell lab at the University of Cambridge is seeking a postdoctoral Research Associate. We are an interdisciplinary group focused on applying mathematical, evolutionary and statistical approaches to understand the dynamical behaviour of mutant clones that expand in our tissues as we age and how these impact cancer risk. We are particularly interested in exploiting deep DNA sequencing data sets from longitudinal samples collected over multiple decades from the same individual to build predictive models of cancer risk.

The goal of this project is to use approaches from statistical physics to understand the evolutionary dynamics of pre-cancerous clones and whether these can be used to predict cancer risk, with a focus on acute myeloid leukaemia ("AML"). Recent large-scale deep sequencing studies have shown that AML arises through an evolutionary process with the stepwise accumulation of multiple (5-10) genetic and epigenetic alterations acquired in self-renewing blood stem cells ("HSCs"). This raises several quantitative questions:

- How is it possible for a single cell to acquire the full complement of 5-10 mutations necessary to generate a leukaemia, when both the mutation rate and cell division rate in HSCs is so low?

- Back of the envelope estimates and recent sequencing data suggest that intermediate clones have a fitness advantage over healthy cells: what level of selection is necessary to explain age incidence curves and clone frequency distributions observed in data?

- Recent work (e.g. Jaiswal et. al. NEJM 2014 and Martincorena et. al. Science 2015) has found large mutant clones exist even in healthy tissues. How much predictive value do these clones have for ascribing cancer risk?

While the dynamics of clonal evolution has been studied extensively over the last decade (e.g. Desai-Fisher Genetics 2007, Levy-Blundell et. al. Nature 2015), there remains to be a model that has applied these insights to make testable quantitative predictions in the context of AML. This project aims to determine what genomic signatures might be indicative of early cancer and apply these to a large number of open-access data sets. There will be opportunities to work together with an outstanding team of collaborators with expertise in statistical physics, evolutionary theory, deep sequencing and cancer genomics. Candidates will also have an opportunity to participate in a collaborative effort involving analysis of ultra-deep sequencing data from over 500 people over multiple decades of life.

Candidates should have a PhD (or equivalent) in a relevant sublject such as physics or quantitative biology an excellent research record and an interest in biological problems. They should be proficient with at least one of: Python, R, Matlab, C++, Fortran. Experience with genomic datasets is a plus, but not essential.

The Blundell lab is part of the Department of Oncology at the University of Cambridge and the Early Detection Programme of the CRUK Cambridge Centre. Situated on Cambridge Biomedical Research Campus, with close ties to Addenbrooke's Hospital and a host of biotech companies, our location affords our members access to human tissue samples, state of the art sequencing facilities, and the opportunity to forge collaborations with world class physicists, engineers, biologists and clinicians in Cambridge. More information on the lab can be found at

Informal enquiries should be directed to Jamie Blundell via e-mail:

Fixed-term: The funds for this post are available for 3 years in the first instance.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a health assessment and a security check.

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/statement of interest 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 13th May 2018, with interviews yet to be confirmed by the department.

Please quote reference RD15260 on your application and in any correspondence about this vacancy.

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