J Robson

This fully funded PhD project at The University of Manchester, in partnership with the UK Atomic Energy Authority (UKAEA), aims to advance fusion energy research by developing novel AI-driven compressed representations for high-dimensional physics data. Tokamak fusion reactors, a promising source of clean energy, require structural materials that can withstand extreme conditions, including high temperatures and intense radiation. Radiation bombardment leads to complex, evolving defect structures within these materials, and predicting such damage is crucial for assessing the risk of component failure. High-fidelity atomistic simulations can model these damage events, but the resulting data—describing the positions of millions of atoms—is too large and high-dimensional for direct use in predictive models. The core challenge is dimensionality reduction and feature extraction: creating efficient, compressed 'fingerprints' of atomic structures that enable actionable insights. The project treats the evolution of material damage as a large-scale pattern recognition problem, focusing on developing machine learning algorithms capable of fingerprinting vast atomic datasets and capturing complex, long-range atomic neighborhoods. You will work at the intersection of geometric deep learning, unsupervised representation learning, and time-series analysis. Key tasks include designing and developing novel descriptors, especially using graph neural network (GNN) methods, training and validating models on a large database of damaged atomic structures, and modeling latent space dynamics using time-series data. The compressed descriptors you create will be essential for building fast, efficient surrogate models for radiation damage, directly supporting the in-silico design and qualification of new fusion reactor components. The project offers access to extensive datasets and collaboration with both academic and industry supervisors. Applicants should have a strong background in mathematics, computer science, or a relevant scientific domain. The program is part of the AI UKRI CDT, offering full funding, home tuition fees, and a tax-free stipend at the UKRI rate (£20,780 for 2025/26), with a start date in September 2026. The University of Manchester is committed to equality, diversity, and inclusion, encouraging applications from all backgrounds and offering flexible study options. To apply, submit your application via the university portal, including all required documents and referee details. For further details, see the official FindAPhD listing.

This fully funded PhD project at The University of Manchester offers an exciting opportunity to advance fusion energy research by developing novel machine learning methods for high-dimensional physics data. The project addresses a critical challenge in the design of tokamak fusion reactors: predicting and modeling radiation-induced damage in structural materials. High-fidelity atomistic simulations generate vast, complex datasets describing the positions and evolution of millions of atoms under extreme conditions. However, the sheer volume and dimensionality of this data make it impractical for direct use in predictive models. The core research goal is to create compressed, information-rich representations ("fingerprints") of atomic structures using advanced machine learning techniques. You will explore and develop new algorithms at the intersection of geometric deep learning, unsupervised representation learning, and time-series analysis. A major focus will be on graph neural network (GNN) approaches to capture long-range atomic neighborhoods and dynamic changes over time. The project provides access to a large, curated database of damaged atomic structures for training and validation, enabling you to design, test, and refine your models on real-world data. Your work will directly contribute to the development of fast, efficient surrogate models for radiation damage, accelerating the in-silico design and qualification of new fusion reactor components. The project is part of the UKRI AI CDT in Decision Making for Complex Systems and is supported by the UK Atomic Energy Authority (UKAEA), offering a stipend of £31,000 for eligible students. The start date is September 2026, and the position is based at The University of Manchester, with industry collaboration. Applicants should have a strong background in mathematics, computer science, or a related scientific field. Required application materials include academic transcripts, CV, a supporting statement, and contact details for two referees. An English language certificate is required if applicable. The university values diversity and inclusion, encourages applicants from all backgrounds, and offers flexible study options, including part-time study. Apply by April 10, 2026, through the University of Manchester Application Portal under 'PhD in Artificial Intelligence'. For more information, visit the project page or contact the industry supervisor at [email protected].

This PhD project at The University of Manchester focuses on the development of solid-state composites for safe and efficient hydrogen storage, a critical technology for advancing clean energy and decarbonization. Hydrogen is recognized for its high gravimetric energy density and zero CO₂ emissions, making it a key player in achieving net-zero targets by 2050. However, current hydrogen storage materials face challenges such as slow absorption/desorption kinetics and limited cycling stability. The research aims to engineer advanced solid-state composites that enhance interfacial bonding, accelerate hydrogen absorption and desorption, and maintain structural stability over repeated cycles. The project will systematically investigate the mechanisms governing hydrogen storage capacity through microstructural analysis, mechanical property evaluation, and hydrogen behavior studies. These insights will inform the rational design of next-generation hydrogen storage materials, supporting the UK's leadership in renewable energy technologies and expanding hydrogen's role in sectors like automotive and aviation. The Department of Materials at The University of Manchester offers a vibrant research environment with access to a range of scholarships, studentships, and awards for both UK and international students. Applicants should hold or expect at least a 2.1 honours degree or a master's in a relevant science or engineering discipline. The university values diversity and encourages applications from all backgrounds, including those returning from career breaks or seeking flexible study arrangements. To apply, candidates should contact the lead supervisor, Dr Peng Gong ([email protected]), to discuss their academic background and motivation. Applications are submitted online and must include transcripts, CV, a supporting statement, and contact details for two referees. English language certification is required if applicable. The application process is competitive, and incomplete applications will not be considered. For more information on funding opportunities, visit the university's funding page or search the funding database. The application window is open year-round, providing flexibility for prospective students.