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The Research Fellow is expected to: (1) Manage and drive one or more research projects, design and execute experiments, assimilate and interpret results; (2) Solve scientific problems, think independently, and
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Responsibilities: Carry out research works on climate-change impact assessment, data assimilation, climate downscaling, bias correction, GIS analysis, statistical analysis etc. Coordinate project report preparation
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publication profiles are particularly welcome, but a willingness to assimilate the complexities of working with innovators of healthcare technology and patient data, and to work as part of a diverse team, are
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devices through to the use of their data in informing personalised healthcare. Allied to other programmes in Singapore and the UK, it represents a unique opportunity to be at the forefront of research and
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to 3. Candidates with strong publication profiles are particularly welcome, but a willingness to assimilate the complexities of working with innovators of healthcare technology and patient data, and to
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Department of Population Health, University of Oxford and the Department of Public Health and Primary Care at the University of Cambridge, as part of the Health Data Research UK’s ‘Molecules to Health Records
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adiposity, physical activity and cardiometabolic disease, who is interested in applying their skills in developing resources, data integration and statistics to advance understanding of environmental, social
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” before the operation and have sub-second performance. They unlock next-generation data assimilation and new predictive decisionsupport AI. This position is primarily dedicated to developing GN-geomodels
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Generative-Network (GN) geomodels. GN-geomodels “learn geology” before the operation and have sub-second performance. They unlock next-generation data assimilation and new predictive decisionsupport AI
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Science, including atmospheric chemistry; climate and atmosphere-ocean dynamics; cloud microphysics, severe storms, and mesoscale meteorology; data assimilation, machine learning, and causal discovery