Research Associate (Fixed Term)
An opportunity has arisen for a talented computational statistician or probabilistic machine learning methods developer to join Dr Paul Kirk's group at the MRC Biostatistics Unit, Cambridge University, within the Biostatistical Machine Learning research theme.
Biostatistical Machine Learning (BML) is a new research theme within the MRC Biostatistics Unit, jointly led by Paul Kirk and Sach Mukherjee. BML focuses on cross-cutting, methodological research in machine learning (ML), artificial intelligence (AI), and high-dimensional statistics. The overarching aim is to combine flexible and scalable AI and ML approaches with the need for robustness, interpretability and scientific understanding that is essential in biostatistical applications.
We are seeking an ambitious and motivated individual to contribute to Paul Kirk's group and the broader BML research theme. Depending on academic background and interests, the successful applicant could contribute to projects related to: (1) probabilistic machine learning approaches for integrative analyses of biomolecular datasets; (2) large-scale Bayesian inference, particularly for models used in the analysis of electronic health record (EHR) data; or (3) Gaussian process regression models to predict dose-response surfaces in combination drug screens.
The team has strong collaborations with groups across the Cambridge Biomedical Campus and beyond. Current and past team members have worked on Bayesian nonparametrics, unsupervised and semi-supervised integrative clustering, multi-view modelling, multiple kernel learning, topic modelling, and matrix factorisation approaches, across an array of applications ranging from cancer subtype discovery to the prediction of complications during pregnancy.
The successful candidate will have a PhD in a strongly quantitative discipline, ideally computational statistics or probabilistic machine learning. 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 is highly desirable, and experience of computational Bayesian methods may be advantageous. Experience with omics or EHR 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 courses and on-the-job training.
Fixed-term: The funds for this post are available for 3 years in the first instance.
We welcome applications from individuals who wish to be considered for part-time working or other flexible working arrangements.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
Please ensure that you upload a covering letter and a CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. 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: 22nd September 2024
The interview date for the role is: To be confirmed
Please quote reference SL43016 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.