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Nigel Wilding

Top university

11 months ago

PhD in Machine Learning Approaches to Simulating Active Matter in New Ensembles University of Bristol in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD Project (Students Worldwide)

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Bristol

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Keywords

Computer Science
Machine Learning
Mathematics
Molecular Dynamics
Computational Physics
Monte Carlo Simulation
Diffusion
Statistical Mechanics
Phase Separation
Active Matter
Critical Phenomena
Soft Materials
Microfluidic
Policy And Planning
Sociobiology
Physics

About this position

This PhD project at the University of Bristol's School of Physics focuses on developing machine learning approaches to simulate active matter in new statistical ensembles. The research aims to overcome the limitations of traditional fixed-(N, V, Pe) simulations by creating machine-learned 'reservoir moves' for grand-canonical–style simulations. The project will leverage symmetry-aware graph neural networks, diffusion models, and policy models trained on canonical trajectories to enable smart particle insertions, deletions, and activity updates that preserve key structural and flow statistics. Validation will involve reproducing one- and two-body observables, mapping MIPS binodals, and exploring wetting/drying and critical scaling, with systematic benchmarking against standard molecular dynamics, Monte Carlo, and heuristic methods.

Large-scale computations will utilize the ISAMBARD AI supercomputer and BlueCrystal Phase 5 HPC cluster. The research is timely, addressing the challenge of accessing phase coexistence, interfacial phenomena, and critical behavior in active systems, which are currently constrained by ensemble limitations. Success will provide a general-purpose toolkit for nonequilibrium ensembles, advancing understanding and design in soft materials, biological collectives, and microfluidic active suspensions.

The project is supervised by Professor Nigel Wilding (lead), Dr. F. Turci, and Dr. S.

Hanna, all experts in theoretical and computational physics. The Physics Graduate School at Bristol offers a vibrant, diverse community, comprehensive induction, targeted skills training, and strong support for career development. Scholarships are available for home students (covering UKRI-rate stipend, tuition, and training), with a limited number of fully-funded places for outstanding international candidates.

Applicants should have a strong background in physics or a related field, with at least a UK upper second-class honours degree or equivalent, and meet English language requirements. Applications require a CV, personal statement, and transcripts, and should be submitted via the University of Bristol's online system, specifying 'Bristol Physics Research Scholarships' as the funding source. The deadline for applications is 19 January 2026. The School of Physics is committed to diversity and equality, encouraging applications from under-represented groups.

Funding details

Funded PhD Project (Students Worldwide)

What's required

Applicants must have a first degree in physics or a related subject, normally at a level equivalent to at least UK upper second-class honours, or a relevant postgraduate master's qualification. International applicants should consult the University of Bristol's country-specific requirements. If English is not your first language, you must meet English Language Profile F as specified by the University of Bristol. No research proposal is required, but you must specify a research theme or academic supervisor. All applicants are assessed on merit and shortlisted candidates will be interviewed.

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Professors

Professor at School of Physics

University Name
Country flag

United Kingdom

Research Interests

Physics

Statistical Mechanics

Active Matter

Microfluidic

Critical Phenomena

Dr. at School of Physics

University Name
Country flag

United Kingdom

Research Interests

Policy And Planning

Diffusion

Computer Science

Physics

Molecular Dynamics

Dr. at School of Physics

University Name
Country flag

United Kingdom

Research Interests

Sociobiology

Mathematics

Machine Learning

Policy And Planning

Soft Materials