Applications are invited for a Research Associate to join Professor James Locke's group at the Sainsbury Laboratory, University of Cambridge. The post will develop and apply machine-learning approaches, including DNA foundation models, to investigate how regulatory DNA sequences influence gene expression dynamics, variability between individual cells and plants, and responses to the environment.
The research
The successful candidate will develop, adapt and evaluate computational models that link promoters and other regulatory sequences to gene expression behaviour. Research questions include which sequence features influence expression variability, whether models can predict the effects of regulatory sequence changes, and how these relationships depend on environmental conditions and circadian timing.
A central focus will be exploring how DNA foundation models and other sequence-based approaches can help address these questions. The project offers scope to develop new methods, compare modelling approaches and identify regulatory features that generate testable biological predictions.
Models will be developed and tested using quantitative gene expression measurements, including fluorescent reporters, time-resolved transcriptomics and single-nucleus RNA sequencing. The post holder will work closely with experimental researchers to connect model predictions with measurements of gene expression and guide experiments that test how regulatory sequences shape expression variability and environmental responses.
The successful candidate will help shape the project's scientific and computational direction, building on their expertise and interests within these research aims.
Person specification
Applicants should hold, or be close to obtaining, a PhD in computational biology, bioinformatics, systems biology, quantitative plant science, computer science, statistics, or a related subject.
Essential requirements are strong programming skills in Python and/or R; experience developing or applying machine-learning methods to biological sequence, gene expression or related datasets; and a good understanding of statistical analysis and model evaluation.
Experience with DNA foundation models, biological sequence modelling, deep learning, regulatory genomics or interpretable predictive modelling would be particularly valuable. Familiarity with plant biology, gene expression variability, circadian biology, transcriptomics or time-series analysis would also be advantageous.
Candidates should be self-motivated, able to work independently, and enthusiastic about close collaboration with experimental researchers.
Research environment and career development
The Locke group works across quantitative plant biology, gene regulatory dynamics and systems biology. The Sainsbury Laboratory offers an interdisciplinary research environment, mentoring and professional development opportunities.
The post holder will be supported in presenting and publishing research and, where appropriate, preparing applications for independent fellowships. Flexible working arrangements may be considered subject to the requirements of the role and University and Laboratory approval.
How to apply
Applicants should submit a full CV, and a cover letter explaining their suitability for the post, and contact details for three referees through the University of Cambridge recruitment system when the vacancy is advertised.
Fixed-term: The funds for this post are available for 16 months 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.
Scientific enquiries apart from the formal application should be directed to Professor James Locke at James.Locke@slcu.cam.ac.uk
For questions regarding the application process, please email HR@slcu.cam.ac.uk
Please quote reference PT51544 on your application and in any correspondence about this vacancy.
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Key information
Department/location: Sainsbury Laboratory
Salary: £38,448-£46,970
Reference: PT51544
Category: Research
Date published: 29 October 2026
Closing date: 12 November 2026