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.
A broad range of statistical and machine learning methods exist to identify low-dimensional structure(s) from high-dimensional omics datasets (e.g. clustering algorithms, matrix factorisation approaches, dimension reduction techniques). However, ensuring that these methods identify optimally relevant structure remains an open problem, and will often depend on the context and the particular application being considered (i.e. how we define what we mean by "relevant"). In this project, we seek to identify low-dimensional representations of high-dimensional omic datasets that allow us to make clinically relevant predictions in cancer.
We are seeking an ambitious and motivated individual to contribute to this research. 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.
The successful candidate will have a PhD in a strongly quantitative discipline, ideally statistics or probabilistic machine learning. Past experience with biomedical applications ¿ particular cancer biology ¿ would be highly 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 latent variable models, dimension reduction techniques, and/or matrix factorization approaches would be advantageous. Past experience with omics datasets and clinical data analysis would be highly desirable. 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 2023 in the first instance.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
Closing date for applications is 21 October 2020 with interview date on the week commencing 2 November 2020.
Please quote reference SL24157 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.