To achieve this your role will involve developing inverse methods and optimisation techniques, including the use of Green functions. You will use your techniques to design state-of-the-art magnetic field systems, sub-systems, and components to drive the development and commercialisation of a range of innovative QT-enabled systems. There will be a strong focus on the optimisation of these systems, which will comprising passive shielding and active field generation subsystems, to satisfy end-user requirements for performance, size, weight, power and cost. You can expect to see your outputs integrated and used in real-world high Technology Readiness Level systems across a range of academic, commercial, healthcare, environmental and security applications.

The role will involve requirements definition, software development for design optimisation, and component fabrication undertaken in close collaboration with academic and industry partners, engineering colleagues, and a wide range of end users. Collaboration with experimentalists, medical physicists, neuroscientists, researchers from other disciplines, and industry/end-user partners will be a vital part of the role.
 
You will be responsible for writing up your work for publication, and will have the opportunity to use your skills, experiences, and creativity to identify new areas for research, and extend your research portfolio.
 
The position is available from 1 July 2026 and is offered on a full time (36.25 hours per week) fixed-term contract until 31/12/2027.

As part of our commitment to improving equality, diversity and inclusion within the school, shortlisted candidates will be given the opportunity to talk to a member of staff representing women, BAME, LGBTQIA+ or disabilities communities. This will be separate to the assessment process and will play no role in the decision to appoint.

Informal enquiries may be addressed to Professor Mark Fromhold, email Mark Fromhold Mark.Fromhold@nottingham.ac.uk. Please note that applications sent directly to this email address will not be accepted.

Closing Date: 31 May 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