Research Assistant in machine learning (Fixed Term)

Job Description:

We are looking to appoint a motivated researcher to conduct computational research in the field of machine learning, specifically at the interface of large language models and statistical mechanics, with applications to scientific areas including the interface of statistical physics and biomaterials, under the EPSRC programme grant “Designing bio-instructive materials for translation ready medical devices."

The role holder will be an integral part of an interdisciplinary team of researchers covering a broad range of topics including statistical physics, machine learning and biomaterials, and based in the Condensed Matter Theory group of the School of Physics and Astronomy. We seek a motivated, skilled and independent researcher to complement our team.

The candidate will carry out the following duties:

• Plan and conduct research using recognized approaches, methodologies and techniques.
• Manage own research activity and resolve problems, if required, in meeting research objectives and deadlines.
• Write up research work for publication.
• Contribute to the dissemination of research results at conferences.
• Build relationships with both internal and external contacts in order to exchange information, to form relationships for future collaborations, and to identify potential opportunities for collaboration.
• Collaborate with academic colleagues on areas of shared interest.
• Provide support, guidance and supervision to other members of the research group, where appropriate, in own areas of expertise.

We are looking for a motivated, skilled and highly independent researcher who can evidence:

• A minimum of an MSc in machine learning or equivalent in relevant subject area.

A strong track record in:

• machine learning and machine learning engineering
• software engineering and development

Proven Experience in:

• research in Large Language Models
• rare event analysis
• Excellent oral and written communication skills, including the ability to communicate with clarity on complex information.
• The ability to build relationships and collaborate with others.
• The ability to creatively apply relevant research approaches, models, techniques and methods.

This full-time position (36.25 hours per week) is available for the earliest start of 01/09/26 for a duration of one year.

Informal enquiries may be addressed to Prof. Juan P. Garrahan ([email protected]). Please note that applications sent directly to this email address will not be accepted.

Closing Date: 15 Aug 2026
Category: Research and Teaching (R&T)