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 (juan.garrahan@nottingham.ac.uk). Please note that applications sent directly to this email address will not be accepted.

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 (juan.garrahan@nottingham.ac.uk). Please note that applications sent directly to this email address will not be accepted.