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.

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

Location: Nottingham

Funding For: UK Students 

Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780 for 2025/26 academic year, increasing in line with inflation). Research training and support grant (RTSG) of £3000 per year. Funding is available for 4 years.

Hours: Full Time

Closes: Open until position filled

The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and established Markov models will be made wherever possible. Expected outcomes include a unified non-Markovian framework for time series analysis, a suite of relevant datasets, and large-scale statistical studies comparing different methods. The successful candidate will be jointly supervised by:

Dr Edward Gillman (https://www.nottingham.ac.uk/physics/people/edward.gillman)

and

Professor Juan P. Garrahan (https://www.nottingham.ac.uk/physics/people/juan.garrahan)

Supervisors: Dr Edward Gillman, Professor Juan P. Garrahan

Entry requirements

Open to UK nationals only (This placement will require national security vetting at the security check (SC) level, which makes the restriction to UK nationals necessary). Expected starting date October 2025. We are seeking candidates with:

• Relevant subject matter experience at required level (e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science)

• Willingness to adapt and work across different disciplines

• Ability to work independently and cooperatively

• Commitment to inclusivity, responsible research and innovation

How to apply

Applications should be submitted by following the steps outlined on the page https://www.nottingham.ac.uk/physics/studywithus/postgraduate/howtoapply.aspx

In the “Research Proposal Section” of the online application simply state that you are applying to the open position on “Machine Learning for Probabilistic Modelling” with Dr Edward Gillman and Professor Juan P. Garrahan as supervisors.

Funding Fully and directly funded for this project only. Full tuition fee waiver p.a. (Home Students only) and stipend at above UKRI rates p.a. (currently at £20,780 for 2025/26academic year, increasing in line with inflation). Funding is available for 4 years

Application deadline: Open until the position is filled

Enquiries: Contact Dr Edward Gillman (edward.gillman@nottingham.ac.uk)

Closing Date: 25 Jul 2025
Category: Studentships