Research Associate (Fixed Term)

Updated: 18 days ago
Location: Cambridge, ENGLAND
Job Type: Permanent
Deadline: 10 Nov 2020

An opportunity has arisen for a talented statistician or probabilistic machine learning methods developer/practitioner to join the MRC Biostatistics Unit, Cambridge University, as part of the RESCUER (RESistance Under Combinatorial Treatment in ER+ and ER­ Breast Cancer) project. RESCUER is a multi-institute EU-funded Horizon 2020 project for the analysis of combinatorial treatment for breast cancer.

The successful applicant will work on the prediction of synergistic effects of drug combinations on the basis of on the basis of single compound responses and omics profiling of cell lines, patients, and organoids. By using these data and by exploiting similarities of drugs and drug targets, we aim to predict synergies of drug combinations at various doses on various biological material. The key idea is to exploit similarity between approved drugs, drug pairs, and cancer genomic profiles and to combine these similarity tensors through state-of-the-art multiple kernel learning methods, which will be implemented in a hierarchical Bayesian context to allow for feature selection and information sharing to increase prediction power.

The successful applicant 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 Bayesian inference, and/or kernel 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 Paul Kirk ( ) or Sylvia Richardson ( ).

Fixed-term: The funds for this post are available for 42 months in the first instance.

The post is full-time but applications are welcome from those who would like to work flexibly.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

The closing date for all applications is 10 November 2020 with interview date to be confirmed.

Please quote reference SL24370 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.

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