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perpetuation (or maintenance/persistence); to build ML models that include the heart’s physical properties to find patterns in the data and predict which areas in the heart drive AF. This project will explore
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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
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Supervisory Team: Prof Neil Sandham PhD Supervisor: Neil Sandham Project description: This project is focused on scale-resolving simulations (large-eddy and direct numerical simulation) combined
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will lead the development of novel motor topologies optimised for this cutting-edge material. Supported by experienced supervisors, the student will be able to design, model, and validate working
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performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with ultrasonic sensors for real-time seal gap measurement. Combine experimental research and mathematical modelling
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strategies for robust statistical models. The project’s scope will be tailored to the candidate’s expertise, offering opportunities for innovation and impact. The successful applicant will join a dynamic
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). Access to cutting-edge facilities, including advanced microscopy, controlled environment growth rooms, genomics, proteomics, and metabolomics platforms. Opportunities to work across model and crop species
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machine structures, together with AI-driven optimization frameworks for diverse applications while considering LCA metrics. The success of this project could serve as a model for other energy-related
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contribute to ongoing cancer risk modelling projects within the Centre for Cancer Genetic Epidemiology (CCGE), based in the Department of Public Health and Primary Care. They should have a strong understanding
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intelligence, multi-agent systems, and the design of AI models. They will also acquire transferable skills in interdisciplinary problem-solving and innovation, which will significantly enhance