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integrating high-resolution socio-ecological, environmental, and novel health data from individual and population sources. This research will advance our understanding of how major infectious diseases
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within the prestigious Roger Williams Institute of Liver Studies, where our researchers are at the forefront of liver disease research. As part of this vibrant academic environment, you will join the Zhang
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responsibility is to test key assumptions about differential disease risk by integrating high-resolution socio-ecological, environmental, and novel health data from individual and population sources. This research
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an unmatched infrastructure in place to support the planned work, including one of the UK’s only 7 Tesla MRI systems located inside a hospital environment, state-of-the-art engineering and physics
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work alongside experts in both computational and experimental chemistry, providing a supportive research environment. Applicants should have a PhD in Chemistry or related field, and extensive experience
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experimental chemistry, providing a supportive research environment. Applicants should have a PhD in Chemistry or related field, and extensive experience in python programming and machine learning models
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cloud or distributed computing environments. Familiarity with self-supervised and contrastive learning techniques for aligning text and images (e.g., CLIP, SimCLR). Clinical experience, e.g., interaction
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work closely with the Principal Investigator (PI) Guillaume Conchon–Kerjan on the EPSRC project ‘New Phenomena for Random Walks in Dynamic Environments’ to deliver innovative research results in
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high dimensions. About the role You will work closely with the Principal Investigator (PI) Guillaume Conchon–Kerjan on the EPSRC project ‘New Phenomena for Random Walks in Dynamic Environments
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on the perturbation of skeletal muscle cells (myofibres) during periods of environmental and/or genetic influence (ageing, amyotrophic lateral sclerosis, sex differences…) and in the analysis of single myofiber data