This position will contribute to the UKRI NERC-funded project "Defining species sensitivities to endocrine-disrupting chemicals" led by Brunel University of London in collaboration with the University of Southampton. The project aims to develop innovative computational approaches to predict which vertebrate species are most sensitive to endocrine-disrupting chemicals and to identify the molecular and structural features underlying differences in sensitivity. It will investigate nine families of nuclear receptors across fish, amphibians, reptiles, birds and mammals.

Job Details

- Position Title: Research Fellow: AI for Molecular Modelling - 16961
- Department: Computer Sciences / College of Engineering, Design and Physical Sciences
- Location: Brunel University of London, Uxbridge Campus
- Salary: Grade R1 from: £41,292 to £43,572 per annum inclusive of London Weighting with potential to progress to £48,557 per annum inclusive of London Weighting through sustained exceptional contribution. (Pro-rata for Part-time)
- Hours: Full-time
- Contract Type: Fixed-term until 30/11/2028
- Posted Date: 10/08/2026
- Closing Date: 21/09/2026
- Closing date for applications: 21September 2026
- Interviews will take place during the week of 12 October 2026 online.
- A Basic Disclosure and Barring Service (DBS) check is required for this role.

Responsibilities





- The Research Fellow will deliver the computational component of the project through protein structure modelling, molecular docking and detailed analysis of protein-ligand interactions.
- They will integrate these results with receptor-interaction data generated by the experimental collaborators at the University of Southampton to develop and evaluate artificial intelligence (AI) methods for predicting chemical binding and species sensitivity.

Requirements

- We are looking for a computational scientist with an interest in interdisciplinary research applied to biological and toxicological sciences.
- Preference will be given to candidates with knowledge and experience of protein molecular modelling, protein-ligand docking, machine learning methods for computational biology, and software development in Python.

Benefits

- We offer a generous annual leave package
- discretionary University closure days
- excellent training and development opportunities
- outstanding occupational pension scheme
- a range of health-related support
- The University is committed to a hybrid working approach.

Brunel University of London wishes to promote an inclusive and diverse workforce and create a culture that values the contribution of all backgrounds and communities. All employees will be recruited, selected and appointed in line with our equality and diversity policy.

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📌 Research Fellow: AI for Molecular Modelling - 16961 (Uxbridge)
🏢 Brunel Law School
📍 Uxbridge