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

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University of Cambridge

Central Cambridge

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

Research Associate

18 May 2026

Location

Central Cambridge

University of Cambridge

Type

Fixed Term

Start Date

1 June 2026

Salary

£37,694-£46,049

Required Qualifications

PhD in quantitative discipline
Deep learning and ML experience
Programming/scripting proficiency
Interest in gene regulation/molecular biology

Research Areas

Deep Learning
AI for Genomics
Gene Regulation
mRNA Processing
Codon Usage Bias
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Research Associate in Deep Learning and AI for Genomics - Bornelöv Group (Fixed Term)

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

DEPARTMENT/LOCATION: Department of Biochemistry, Central Cambridge

SALARY: £37,694-£46,049

REFERENCE: PH49454

CLOSING DATE: 18th May 2026

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 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 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 below 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 (smb208@cam.ac.uk).

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

To apply for this vacancy, please click on the 'Apply' button below. 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.

Further Particulars - Research Associate (Bornelov)

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Frequently Asked Questions

🎓What qualifications are required for this Research Associate position?

Applicants must hold a PhD (or be near completion) in a relevant quantitative discipline. Essential skills include experience in deep learning, machine learning, or bioinformatics, proficiency in programming/scripting, and ability to drive projects independently. Prior work in gene regulation, mRNA transcription, translation, or turnover is highly beneficial. Explore similar roles in our research jobs section or postdoc opportunities. 🎓

🔬What is the focus of the research project in the Bornelöv Group?

The project uses deep learning and AI to model how codon usage bias and mRNA features influence gene regulation, including mRNA localisation, stability, translation, and protein function. You'll apply modern sequence-based models with access to substantial GPU compute and high-performance resources. Shape your own research questions within this area. Learn more about postdoctoral success via our postdoc guide.

📝How do I apply for this Deep Learning AI Genomics role?

Click the 'Apply' button to register with the University's recruitment system. Submit a CV, publication list, cover letter explaining your fit, and contact details for two academic referees. Quote reference PH49454. Closing date: 18 May 2026. Use our free cover letter template and resume template for academic applications.

💰What is the salary, duration, and employment type?

Salary: £37,694-£46,049. This is a fixed-term position funded from 1 June 2026 to 31 May 2029. Successful candidates are encouraged to apply for postdoctoral fellowships. Check professor salaries and university salaries for context in the UK.

💻What resources and support are provided?

Access to substantial GPU compute, high-performance computing, and the computational team in the Bornelöv Lab. Freedom to shape the project direction. Informal enquiries to Dr Susanne Bornelöv at smb208@cam.ac.uk. Visit www.sblab.uk for publications. See research assistant jobs for similar computational roles.

📚Is teaching required in this role?

No teaching load is mentioned; this is a fully computational research associate position focused on deep learning for genomics. Ideal for those prioritizing research over teaching. Compare with faculty positions that may include teaching.
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