Brunel University of London
Research Fellow: AI for Molecular Modelling - 16961
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Research Fellow: AI for Molecular Modelling – 16961
Department/College
Computer Sciences / College of Engineering, Design and Physical Sciences
Location
Brunel University of London, Uxbridge Campus
Salary
Grade R1 from: £41,292 to £44,762 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
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.
College / Directorate
College of Engineering, Design & Physical Sciences
Department
Department of Computer Science
Full Time / Part Time
Full Time
Posted Date
10/08/2026
Closing Date
21/09/2026
Ref No
5193
Documents
Job Description (Word, 86.75kb)
Position Title
Research Fellow: AI for Molecular Modelling – 16961
About Brunel University of London
Brunel University of London was established in 1966 and is a leading multidisciplinary research-intensive technology university delivering economic, social and cultural benefits. For more information please visit: https://www.brunel.ac.uk/about/our-history/home
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About the Department
The Department of Computer Science at Brunel, where this project will be based, has a strong record of internationally recognised research. In the 2020–2025 editions of the NTU Performance Ranking of Scientific Papers for World Universities, Computer Science at Brunel was ranked in the top 10 in the UK overall. It was also ranked first in the UK for H-index and highly cited papers for five consecutive years. More recently, Brunel was ranked 48th worldwide for Artificial Intelligence in the 2025 Shanghai Global Ranking of Academic Subjects.
Role Overview
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.
Key Responsibilities
- Deliver the computational component of the project through protein structure modelling, molecular docking, and detailed analysis of protein-ligand interactions.
- Integrate these results with receptor-interaction data generated by the experimental collaborators at the University of Southampton.
- Develop and evaluate artificial intelligence (AI) methods for predicting chemical binding and species sensitivity.
Qualifications and Experience
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- 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
- Software development in Python
Application Process
- Please upload your CV (including publications) and a Cover letter summarising your experience and achievements in the application system.
- For an informal discussion, please email Dr Alessandro Pandini at alessandro.pandini@brunel.ac.uk
Benefits
- Generous annual leave package plus discretionary University closure days
- Excellent training and development opportunities
- Great occupational pension scheme
- Range of health-related support
- Hybrid working approach
Closing Date for Applications
21 September 2026
Further Information
For further details about the post including the Job Description and Person Specification and to apply please visit: https://careers.brunel.ac.uk
Technical Support
If you have any technical issues please contact us at: hrsystems@brunel.ac.uk
Eligibility
All Applicants should be eligible to live and work in the UK for the duration of any offer of appointment. A Basic Disclosure and Barring Service (DBS) check is required for this role.
Equality and Diversity
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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