Research Fellow in Artificial Intelligence and Machine Learning for Scientific Discovery

The Chica Research Group, based within the Manchester Institute of Biotechnology and the Department of Chemistry at the University of Manchester, develops computational and experimental approaches for protein engineering and enzyme design.

We are seeking an outstanding researcher with expertise in artificial intelligence, machine learning, computer science, applied mathematics, statistics, or a related quantitative discipline to join our highly interdisciplinary research environment.

The successful candidate will work closely with computational and experimental researchers to develop and apply modern data-driven approaches to challenges in biomolecular design and engineering. Prior experience in chemistry, biology, or protein science is advantageous but not required.

This position offers an exciting opportunity to contribute to the development of next-generation computational approaches for scientific discovery while helping to expand AI and machine-learning capabilities within the research group.

Overall Purpose of the Job

The postholder will develop and apply machine-learning and artificial-intelligence approaches to challenges in protein engineering and enzyme design.

Working closely with researchers across the group, the successful candidate will analyse complex biological, chemical, and experimental datasets; develop predictive models and computational tools; and identify opportunities where machine learning and artificial intelligence can accelerate scientific discovery.

The role offers substantial scope for methodological innovation and provides an opportunity to contribute to the future development of AI-enabled research within the group.

You will be responsible for:

• Developing and applying machine-learning approaches to challenges in protein engineering and enzyme design.
• Analysing, integrating, and modelling large biological, chemical, and experimental datasets.
• Designing, implementing, training, and evaluating predictive machine-learning models.
• Developing computational tools, workflows, software, and methodologies that support data-driven scientific discovery and expand the group's computational capabilities.
• Providing specialist expertise, guidance, training, and support in machine learning, data science, and artificial intelligence across the research group.
• Collaborating with researchers from diverse disciplinary backgrounds to identify and address scientific challenges using AI and machine-learning approaches.
• Working closely with postdoctoral researchers, postgraduate students, and other team members on computational and experimental projects.
• Contributing to collaborative research projects, publications, conference presentations, and funding applications.
• Maintaining high standards of software development, documentation, and reproducible research.
• Contributing to the strategic development of the group's computational and AI research activities.
• Keeping abreast of developments in artificial intelligence, machine learning, data science, and scientific computing.

We are particularly interested in applicants who:

• Enjoy working in highly collaborative and interdisciplinary research environments.
• Are motivated to develop innovative computational approaches to scientific challenges.
• Are enthusiastic about learning new technologies and methods and applying them to frontier research problems.

Our benefits include:

• Generous employer contribution pension
• 29 days annual leave plus bank holidays, along with Christmas closure
• Ride to work and EV car scheme available

For more information, please see University of Manchester Benefits. You can also find information on our Flexible and Hybrid working here.

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Enquiries about the role, shortlisting and interviews

Name: David Leys

Email Address: david.leys@manchester.ac.uk

General enquiries and administrative support: recruitmentservices.people@manchester.ac.uk

Technical and job portal support: jobseekersupport.jobtrain.co.uk/support/home

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