Research Associate in Deep Learning and AI for Genomics - Bornelöv Group (Fixed Term)

Location: Central Cambridge

We are seeking a Research Associate with strong quantitative and computational skills to join us in the Bornelöv Lab, Department of Biochemistry, University of Cambridge, to study gene-regulatory processes using deep learning. This is a timely opportunity to use recently developed AI-based methods to uncover the molecular mechanisms behind mRNA processing and fate.

You will be part of a computational team, led by Dr Susanne Bornelöv, which studies the role of codon usage bias in gene regulation using complementary approaches including machine learning and AI, evolutionary genomics, and bioinformatics.

Your project will use deep learning to quantitatively model how codon usage bias and other mRNA features contribute to gene regulation. The aim is to gain a precise understanding of how these different properties interact to influence mRNA localisation, stability, and translation, as well as protein function. To achieve this, you will have access to substantial GPU compute and high-performance computing resources and will apply modern sequence-based deep learning models that enable you to systematically probe the effect of differences in codon usage and nucleotide sequence on mRNA fate. The successful candidate will have the freedom to help shape the direction of the project and develop their own research questions within this area.

To be successful in this role, you will need experience in deep learning and other machine learning and/or bioinformatics techniques, an ability to drive a project independently, and be proficient in programming/scripting. Applicants should have a PhD (or be about to receive one) in a relevant quantitative discipline. We are particularly interested in candidates who combine strong quantitative skills with a genuine interest in fundamental molecular biology principles and prior work involving any aspect of gene regulation, including mRNA transcription, translation or turnover would be highly beneficial. Although funding is available for this position, the successful candidate will also be encouraged and supported to apply for postdoctoral fellowships.

Fixed-term: The funding for this position is available from 1st June 2026 until 31st May 2029, in the first instance.

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

For more information about the research group, including our most recent publications, please see our website: www.sblab.uk. Please direct any informal enquiries to Dr Susanne Bornelöv ().

The closing date for applications is Monday 18th May 2026.

To apply for this vacancy, please click on the 'Apply' button above. This will route you to the University's Web Recruitment System. Please send applications in the following format: a CV, a list of your publications, a cover letter, and the names and contact details of two academic referees. Please use the cover letter to explain why you are applying for this role, what you will bring to the project, and how you match the essential and desired criteria for the post.

Please quote reference PH49454 on your application and in any correspondence about this vacancy.

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