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We invite applications for a fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is available to UK (Home) candidates only
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equivalent) in any relevant chemical engineering or science subject.Funding is co-funded through Engineering and Physical Sciences Research Council (EPSRC) and BASF . Project summary The Industry Case (IDLA
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to the primary aluminium production, therefore plays an important role in delivering Net Zero. The traditional Al recycling process requires an enormous investment in the scrap Al sorting and the extra cost
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exhaustive, and the PhD candidate will be required to undertake such tasks and responsibilities as may reasonably be expected of a PhD candidate. Application process The application should include: a
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Award duration: 4 years Food processing currently relies heavily on the combustion of natural gas to provide process and space heating. Until recently, natural gas was considered the preferred fuel
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be expected to receive a minimum 2:1 or 1st class degree in aerospace engineering, mechanical engineering, physics or a related engineering discipline. A Masters qualification is an advantage but not
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information about the application process, please consult our PhD programme webpage or apply directly by clicking the 'Apply' button, above. Before applying, you should carefully review the research carried
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, Environmental Sciences, or a closely related discipline. You will bring strong technical skills in building physics, dynamic thermal modelling, and environmental monitoring. Ideally, you will have experience with
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to interpreting physical symptoms. When an infection is circulating, how do people decide what their own cough, aching muscles or a sense of fatigue mean and what actions, if any, they need to take? Are
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physics and diagnostics at the York Plasma Institute Computational combustion modelling using High-Performance Computing (HPC) Machine learning techniques for predictive combustion models Research