Advanced Modelling of Waste Immobilisation Materials for Long‑Term Nuclear Waste Storage

Reprocessing spent nuclear fuel generates waste that contains long-lived radioactive isotopes capable of remaining hazardous for thousands of years. To protect human and environmental health, this waste must be immobilised within stable inorganic materials before placement in deep geological repositories. Selecting and optimising these waste-form materials is therefore critical to ensuring safety over geological timescales. This self-funded PhD project focuses on the advanced computational modelling of leading waste-form families, including borosilicate and phosphate glasses, titanate-based ceramics, and composite materials such as Synroc. These materials can incorporate a broad range of radionuclides, yet their long-term behaviour depends on complex processes such as irradiation-induced damage, defect accumulation, cracking, aqueous corrosion, and interactions with repository groundwater. Understanding these mechanisms at the atomic level is essential for predicting material performance and designing next-generation waste forms with improved durability. The project will combine multiple simulation methods to build a comprehensive picture of waste-form behaviour. Density Functional Theory (DFT) will be used to capture accurate atomic-scale interactions, including defect energetics, bonding environments, and radionuclide incorporation. Molecular dynamics (MD) simulations will explore larger-scale structural evolution, including radiation-damage cascades, diffusion processes, and long-timescale degradation mechanisms. A key component of the project is the development of machine-learning interatomic potentials using approaches such as the Atomic Cluster Expansion (ACE) and its extensions to multi-element systems. These models will bridge the gap between DFT accuracy and MD efficiency, enabling predictive simulations of complex glass and ceramic systems that are currently out of reach. The main output will be a transferable, DFT-trained machine-learning potential capable of modelling a range of waste-form compositions. This will allow systematic exploration of how factors such as chemistry, waste loading, and processing conditions influence long-term stability. The project will also generate new insights into defect evolution, crack propagation, and the early-stage mechanisms controlling corrosion and radionuclide release. These findings will support both academic research and national efforts to ensure the safe geological disposal of high-level nuclear waste. The project is suitable for candidates with a background in chemistry, physics, materials science, or a related discipline. Experience in computational materials modelling, atomistic simulation, or high-performance computing is beneficial but not essential. The student will gain expertise in electronic-structure calculations, molecular dynamics, machine-learning potential development, and multiscale simulation workflows. This training provides an excellent platform for careers in nuclear materi

The standard University IELTS English language requirement is 6.5 overall with 6.0 in each individual element (reading, writing, listening and speaking).

International students: £29,500

UK students: £5,238

UK/Home: £5,238

International: £29,500

July 2026, October 2026, February 2027, July 2027

1 April 2027

• Loughborough, United Kingdom