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MECHANISM will advance our fundamental knowledge of the in-service operation, processing characteristics and mechanical/physical behavioural response of advanced structural materials within extreme
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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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or international conferences. The successful applicant will have a keen enthusiasm for research in fluid mechanics, with a background in Engineering, Mathematics or a related discipline. Depending on experience and
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for: A first or upper-second-class degree in an appropriate discipline such as, materials science and engineering, nuclear engineering, fusion energy, chemical engineering, physics, chemistry, mechanical
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should have for this project, in addition to our standard entry requirements . Essential Criteria BEng/BSc/MSc in Mechanical Engineering, Applied Mathematics, Applied Mechanics, or related discipline such
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£20,780 per year, and includes a 3-month fusion engineering CDT training programme as part of the 2026 Cohort. This project is co-supervised by Dr Chris Hardie from UKAEA. The UoB Materials for eXtremes
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science, applied mathematics, or a closely related field. Experience or interest in
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load emulation, surface tribology and lubricants, contact mechanics or dynamical phenomena. This is an opportunity to work within a world-class multidisciplinary team within the Engineering Systems
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Applications are invited for a fully funded, full-time PhD studentship in the Department of Mechanical and Aerospace Engineering, supported by Vestas Technology (UK) Ltd, one of the largest wind
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour