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population-based studies. Using data from 11 million vessel segments from over 200,000 CFPs, we have shown that retinal vasculometry can predict cardiovascular disease as well as established risk scores and
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the spaces, but also behavioural changes, such as changing habits regarding ventilation or heating. The second part will employ secondary data analyses from existing cohort studies to explore the associations
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, while simulations are subject to error due to uncertainty in nuclear data and unresolved physical processes e.g. thermal expansion and fine-scale inhomogeneities. Generating independent simulation
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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slice models with Dr Chan’s research on mTOR signalling and metabolic drivers of epilepsy in mTOR related disorders. For information about the UoB Centre for Neurogenetrics, see: https
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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. They therefore contain critical information on the nature of the volcanic system, immediately prior to hazardous explosive events. Understanding them will generate critical information on the transition between
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Institute of Photonic Technologies to be undertaken within the College of Engineering and Physical Sciences at Aston University. Financial Support This studentship includes a bursary to cover the fees rate
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a list of publications, a cover letter highlighting your suitability for the position and why you want to join the Jönsson group and the RESYDE project, and contact information of three referees
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available, tailored to the needs and aspirations of the successful applicant. More information on the academic supervisor and EDC collaborations with JLR is available here: https://www-edc.eng.cam.ac.uk