Research Associate

Sainsbury’s Group; Cambridge; Presencial; GBP 38.000 - 52.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible working

Descripción de la vacante

The Sainsbury Laboratory, University of Cambridge invites applications for a Research Associate to develop and apply machine-learning approaches, including DNA foundation models, to study how regulatory DNA sequences influence gene expression dynamics in plants.

The successful candidate will build computational models linking promoters and regulatory sequences to expression behaviour, compare modelling approaches, and work closely with experimental researchers to connect predictions with

Formación

• PhD in computational biology, bioinformatics, systems biology, or related field.
• Strong programming skills in Python and/or R.
• Experience applying ML to biological sequence or gene expression data.
• Familiarity with DNA foundation models and regulatory genomics.

Responsabilidades

• Develop computational models linking promoters to gene expression.
• Evaluate models using time-resolved measurements and single-cell data.
• Collaborate with experimental researchers to test predictions.

Conocimientos

Python

R

Machine learning

DNA foundation models

Statistical analysis

Time-series analysis

Collaboration

Educación

PhD in computational biology

Herramientas

Python libraries

R packages

Deep learning frameworks

Descripción del empleo

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, Scientific enquiries apart from the formal application should be directed to Professor James Locke at

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.