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of donated human post-mortem brain tissue samples. During this studentship, the successful candidate will develop and characterise novel patient iPSC-derived neuronal-glial organoids to model
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The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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determine the impact of community acquired pneumonia that requires hospitalisation has on the quality of life of patients. The final stage will be to design a generic economic model to evaluate any new
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. The project delves into areas such as hardware-based security measures, tamper detection, and the integration of explainable AI models within embedded platforms. Situated within the esteemed IVHM Centre and
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for the project will include: A thorough review of (1a) interface capturing approaches for flow boiling simulations including adaptive mesh refinement, (1b) available models for predicting the density of nucleation
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satellites, with the potential for travel to test instrumentation in ideal locations. Additionally, the simulation work will focus on developing computational models to validate instrumentation and optimising
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the molten pool. However, these models are computationally intensive and impractical for widespread simulations of large-scale part deposition. This project aims to develop a novel FEA-based approach
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information or questions about this PhD please contact nicola.love@ukhsa.gov.uk . To be considered for this PhD please send a copy of your CV and a personal statement up to 500 words explaining your suitability
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Research Groups at the Faculty of Engineering, which conduct cutting-edge research into electric propulsion systems, composite materials, and advanced simulation technologies. Vision We are seeking a highly