Junior Group Leader in AI/ML for Biomedicine (Fixed Term)

Join us at the MRC-BSU, one of the world's leading centres for methodological research in the biomedical data sciences. We are a unique unit within the University of Cambridge, core funded by the UK's Medical Research Council (MRC), with a focus on developing next-generation statistical, machine learning (ML) and AI methods to drive forward advances in biomedicine and health. The Unit comprises more than 100 researchers, working on the development of novel methods as well as on challenging applications in collaboration with biological, clinical and health research colleagues across Cambridge, the UK and internationally.

We are now looking for a Junior Group Leader (JGL) in AI/ML for Biomedicine to complement our team. The startup package for the successful applicant includes funding for 1 postdoctoral research associate, a personal travel budget, and the opportunity to recruit fully-funded students through the BSU's PhD programme.  This is an independent faculty position: as a JGL you will lead your own research team, working in the area of AI/ML methods for biomedicine and health. You are expected to develop and pursue your own research agenda, supervise junior staff (postdocs and PhD students) and contribute to strategic development as a member of the faculty.

We are specifically looking for candidates with strong methodological interests, i.e. whose focus is on novel methods per se, rather than efforts centred on the application of existing methods to biomedical data.

We welcome applications from outstanding researchers with the vision and ambition to develop a distinctive and world-class research programme. In line with the methodological nature of the position, we seek candidates with a strong technical background in AI, ML or ML-related areas of statistics (and an interest in the biomedical and health domains). While previous work on biomedical problems would be relevant, this is not essential and we are open to candidates with a demonstrably strong technical background and a clear interest in contributing in the biomedical and health domains. We are open in terms of specific research foci. Current key directions at the Unit include robust AI (including scientifically-constrained models), Bayesian nonparametrics, large-scale causal learning, methods for heterogeneous data and learning for latent dynamical processes.

The Unit has a strong track record of developing future leaders and a wide array of mechanisms by which to support your development. The position includes, in addition to the start-up package detailed above, access to extensive support services and computational infrastructure (including HPC/GPUs). We are located on the Cambridge Biomedical Campus, one of the world's pre-eminent centres for biomedical research and innovation and enjoy close collaborative links with world-leading centres including the Cambridge University hospitals, the Wellcome Sanger Institute, the European Bioinformatics Institute, the Cancer Research UK Cambridge Centre and more.

Fixed-term: The funds for this post are available until 31 March 2029 in the first instance.

To apply online for this vacancy and to view further information about the role, please click the apply button above.

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 SL43015 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.

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