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Field
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’ (PHOENIX), led by Associate Professor Thomas Aubry (University of Oxford). Using a combination of laboratory experiments, field work and numerical modelling, PHOENIX aims to improve our understanding
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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split 0.5FTE on the UKRI Medical Research Council funded project STARS: Sharing Tools and Artefacts for Reproducible Simulations in healthcare ; and 0.5FTE on the Health Service Modelling Associates (HSMA
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Associate with mathematical modelling and numerical/data analysis background to join our food system resilience project, led by University of Reading, joining a large interdisciplinary team with an excellent
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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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learning “emulators” of multiple ice sheet and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform
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including cell culture, organ-chip models, tissue engineering, and musculoskeletal biology. The PDRA will plan and conduct experiments, generate high-quality data, prepare publications, make presentations and
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strategies, discovered within our group, to combat heart failure. The work will integrate studies using isolated cell systems with advanced in vivo models, with a particular focus on characterising newly