An opportunity has arisen for a talented statistician or probabilistic machine learning methods developer to join Dr Paul Kirk's group at the MRC Biostatistics Unit, Cambridge University.
The availability and diversity of increasing quantities of patient data present significant opportunities to use statistical and machine learning tools to identify patterns among patients and make personalised predictions. To exploit these data in a meaningful and reproducible manner requires the development of models that can synthesise information from diverse datasets. Our goal is to develop principled analysis methods for molecular precision medicine, with the ultimate goal of improving patient health through the better targeting of treatments.
We are seeking an ambitious and motivated individual to contribute to this research team. The proposed initial project focuses on the development and refinement of models that combine multiple omics datasets with relevant clinical information, with the aim of identifying patient subgroups and disease subtypes that are both reproducible and clinically actionable. The team has strong collaborations with groups across the Cambridge Biomedical Campus (and beyond). Current team members are working on unsupervised and semi-supervised integrative clustering, multi-view modelling, multiple kernel learning, topic modelling, Bayesian nonparametrics, and matrix factorisation approaches, across an array of applications ranging from cancer subtype discovery to the prediction of cardiovascular disease risk.
The successful candidate will have a PhD in a strongly quantitative discipline, ideally statistics or probabilistic machine learning. Past experience with biomedical applications would be advantageous, but not essential. However, a desire to address questions of substantive biological importance and disease relevance is essential. Good communication skills and an enthusiasm for collaborating with others are also essential. Strong programming ability would be desirable, and experience of computational Bayesian methods ¿ particularly in the context of latent variable models ¿ would be advantageous. Past experience with omics datasets would be desirable, but not essential; training will be given on the basic concepts necessary to the post. 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 email@example.com
Fixed-term: 3 years.
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Closing date for applications is 21 October 2020 with interview date on the week commencing 2 November 2020.
Please quote reference SL24154 on your application and in any correspondence about this vacancy.
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