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Field
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PhD Studentship – New approaches for studying the structure of high-temperature molten materials Transition: (October 2025 start) Supervisor 1: Emma Barney Supervisor 2: Oliver Alderman (ISIS
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£20,780 per year, and includes a 3-month fusion engineering CDT training programme as part of the 2025 Cohort. This project is co-supervised by Dr Chris Hardie from UKAEA. The UoB Materials for eXtremes
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testing, enhancing both computational and experimental skills. Additionally, the possibility of contributing to cutting-edge research in a high-impact field means that the student will be part of a
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environment. Accurately predicting flow and heat transfer in these systems is critical for safety, performance, and design assessments, yet direct high-fidelity simulations, such as Large Eddy Simulation (LES
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence will inform future race strategy and live race tactics. Multiple factors influence the strategy...
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kernels that are common in scientific workloads and rewrite them to achieve better performance while delivering high accuracy despite running on low-precision hardware. Hardware is designed to cater
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that can be implemented across the UK. Our innovative PhD project focuses on Heat-as-a-Service (HaaS), a ground-breaking approach to ensuring warm homes while tackling fuel poverty and supporting
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Short overview of PhD opportunity The PhD studentship aims at studying and developing more comprehensive user interfaces for computer-aided decision-making processes, which can benefit from 3D/360
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features and close to material failure at high rate (e.g. extreme deformation, contacts, failure). Furthermore, you will have the opportunity to assess the scalability of IGA in Massively Parallel Processing