PhD Studentship in Physics (University of Nottingham)

Area: Physics & Astronomy Location: Nottingham, UK Reference: SCI3042 Qualification Type: PhD Funding for: UK Students Funding Amount: Full tuition fee waiver per annum (Home Students only) Stipend at above UKRI rates per annum (currently £20,780 for 2025/26 academic year, increasing with inflation) Research Training and Support Grant (RTSG) of £3,000 per year Funding is available for 4 years Hours: Full Time Closing Date: Open until position filled Project Description The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. The project will: Examine and develop methods that go beyond the Markovian paradigm Focus on time series data with properties of uncertainty, irregularity, and mixed-modality Explore models including state-space models, tensor networks, and machine learning frameworks such as recurrent neural networks and transformers Benchmark models and datasets in tasks relating to prediction/forecasting and anomaly detection Compare results with known analytic methods and established Markov models Expected outcomes: A unified non-Markovian framework for time series analysis A suite of relevant datasets Large-scale statistical studies comparing different methods Supervisors: Dr Edward Gillman Professor Juan P. Garrahan Entry Requirements Open to UK nationals only (national security vetting at SC level required) Expected starting date: October 2025 2.1 or above undergraduate degree in physics, mathematics, or computer science Willingness to work across disciplines Ability to work independently and cooperatively Commitment to inclusivity, responsible research, and innovation How to Apply Submit your application through the Nottingham University online portal In the “Research Proposal Section,” 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. Application Deadline: Open until the position is filled Enquiries: Contact Dr Edward Gillman (edward.gillman@nottingham.ac.uk) If you apply for this position please say you saw it on Physicaloxy

Area: Physics & Astronomy
Location: Nottingham, UK
Reference: SCI3042
Qualification Type: PhD
Funding for: UK Students

Funding Amount:

Full tuition fee waiver per annum (Home Students only)
Stipend at above UKRI rates per annum (currently £20,780 for 2025/26 academic year, increasing with inflation)
Research Training and Support Grant (RTSG) of £3,000 per year
Funding is available for 4 years

Hours: Full Time
Closing Date: Open until position filled

Project Description

The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and statistical mechanics for the study of stochastic dynamics with application to the analysis of time series.

The project will:

Examine and develop methods that go beyond the Markovian paradigm
Focus on time series data with properties of uncertainty, irregularity, and mixed-modality
Explore models including state-space models, tensor networks, and machine learning frameworks such as recurrent neural networks and transformers
Benchmark models and datasets in tasks relating to prediction/forecasting and anomaly detection
Compare results with known analytic methods and established Markov models

Expected outcomes:

A unified non-Markovian framework for time series analysis
A suite of relevant datasets
Large-scale statistical studies comparing different methods

Supervisors:

Dr Edward Gillman
Professor Juan P. Garrahan

Entry Requirements

Open to UK nationals only (national security vetting at SC level required)
Expected starting date: October 2025
2.1 or above undergraduate degree in physics, mathematics, or computer science
Willingness to work across disciplines
Ability to work independently and cooperatively
Commitment to inclusivity, responsible research, and innovation

How to Apply

Submit your application through the Nottingham University online portal
In the “Research Proposal Section,” 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.
Application Deadline: Open until the position is filled

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

If you apply for this position please say you saw it on Physicaloxy

Apply

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