Research Assistant - Deep learning for genomics (Fixed Term)

We are seeking a highly motivated and talented research assistant to join our team at the CRUK Cambridge Institute (part of the University of Cambridge) to study genomics and gene regulation using deep learning approaches. This is an exciting opportunity to use AI-based methods to uncover the molecular mechanisms behind gene regulation and mRNA stability.

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 various approaches including machine learning and AI, evolutionary genomics, and sequencing bioinformatics. Our team is funded by an 8-year Wellcome Career Development Award.

Your project will focus on using deep learning and other statistical and machine learning approaches to reveal how codon usage bias and other mRNA features contribute to gene regulation. The ultimate 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 use cutting-edge computational approaches, including building in silico models that enable you to systematically probe the effect of differences in codon usage and nucleotide sequence on mRNA fate.

To be successful in this role, you will need experience in deep learning or other machine learning techniques, an ability to drive a project independently, and solid programming/scripting skills. Applicants should have a BSc or MSc degree in a relevant quantitative discipline and ideally some research experience. Prior work involving any aspect of gene regulation, including mRNA transcription, translation or turnover would be beneficial, but is not strictly required. Most importantly we are looking for someone with a strong desire to be part of a team aimed at uncovering fundamental aspects of gene regulation using computational approaches.

Fixed-term: The funds for this post are available for 1 year in the first instance.

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 ([email protected] ).

The closing date for applications is 5th April 2024, with interviews held in April. The starting date is flexible, but ideally around July.

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

Please send applications in the following format: a CV, including full details of all University courses taken with date (with grades if available), 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 (see the Further Particulars).

Please quote reference SW40985 on your application and in any correspondence about this vacancy.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a security check.

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.

Further information
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