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Open to: UK fee eligible applicants only Funding providers: EPSRC and LaVision UK Ltd Subject areas: Biomedical Engineering, Experimental Mechanics, Image-Based Measurements, Biomechanics Project
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power generation companies—to develop robust methodologies for investigating failure mechanisms in CFRP sleeves. Your contribution will help prevent catastrophic magnet detachment and significantly
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enhance public health. This PhD is funded as part of the HDR UK Inflammation and Immunity Driver Programme, which seeks to transform the UK’s capabilities to improve understanding of mechanisms and health
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for investigating failure mechanisms in CFRP sleeves. Your contribution will help prevent catastrophic magnet detachment and significantly enhance system reliability and safety, aligning with the UK’s NetZero targets
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workshops.The PhD students will focus on using state-of-the-art computational and statistical techniques to shed light on the fundamental mechanisms of decision-making.We are particularly interested in candidates
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the MRC Centre for Neurodevelopmental Disorders. At the Centre for Developmental Neurobiology (CDN), we investigate the mechanisms governing the formation of the brain during embryonic development and in
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the current thermo-mechanical process use to strengthen the current generation of crush alloys. Programme will use different thermomechanical processing paths including heat treatment and more complex paths
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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boundaries. This fully funded PhD scholarship is suitable for students with a background in Engineering Design, Smart Manufacturing, Aerospace Engineering, Mechanics, Computer Science, Mathematics, Life Cycle
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computational methods to optimise the quality of doubly curved shell structures manufactured from recycled, short-fibre composites. A particular novelty of the research will be the inclusion stochastic elements