P. Nithiarasu

PhD studentship at Swansea University in Mechanical Engineering , focused on fusion energy , digital modelling , and stochastic failure prediction using a stochastic finite element approach (RS974). The project investigates material failure under neutron loading in fusion energy components, especially the first layer of the breeder blanket. It builds on Swansea’s multiscale platform combining Monte Carlo thermal loading simulation with material transformation scenarios under realistic fusion-specific neutron irradiation. Research will extend this work by modelling stochastic variability in neutron loading, blanket geometry, and irradiation-driven microstructural evolution. The aim is to improve long-term prediction of structural integrity and lifetime for fusion blanket materials, with validation using emerging experimental data from future fusion-relevant facilities. Funding: full tuition fees plus an annual stipend at the UKRI rate (£20,780 for 2025/26), with additional research expenses of up to £1,000 per year. Eligibility: applicants should hold a 2.1 undergraduate degree (or equivalent) in Engineering or a relevant science discipline. IELTS 6.5 overall with 5.5 in each component, or equivalent, is required. Some applicants may need ATAS clearance later in the process. Deadline: 6 July 2026. Start date: October 2026. How to apply: submit the online application, include RS974 in the required fields, upload the advert as the proposal, choose Scholarship Funding only, and attach the required documents (CV, transcripts, degree certificates, cover letter/personal statement, reference, and English evidence if applicable).

PhD studentship at Swansea University in Mechanical Engineering , focused on fusion energy , digital twins , physics-based modelling , AI , and data-driven validation . The project investigates how experimental data, synthetic datasets, and physics-informed models compare when building reliable digital twins for fusion devices and components. The research will examine fundamental thermomechanical and thermofluid phenomena, uncertainty quantification, and the limits of replacing physics-based models with AI systems. It is especially relevant for students interested in model validation, hybrid modelling strategies, and the reliability of digital twin methods under real operating conditions. Supervisors: Professor P. Nithiarasu, Dr Alberto Coccarelli, and Lloyd Fletcher (UKAEA). Informal enquiries may be directed to Professor Perumal Nithiarasu. Funding: full tuition fees plus an annual UKRI stipend (£20,780 for 2025/26; £21,805 for 2026/27 onwards) and up to £1,000 per year in research expenses. Eligibility: PhD applicants should hold a 2.1 undergraduate degree or equivalent in Engineering or a closely related science discipline. IELTS 6.5 overall with 5.5+ in each component, or an accepted equivalent, is required where applicable. Some applicants may need ATAS clearance. Deadline: 2026-07-06. Applications may be submitted in Welsh. Applicants must apply online using RS973 and upload the required documents, including CV, transcripts, degree certificates, cover letter/personal statement, reference, and supporting evidence as applicable.