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will deliver projects that leverage large-scale electronic health record data and rich cytometry data derived from full blood count analysers to develop and refine machine learning models to improved
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be finalised by July 2026. Supported Areas for START Infocomm Technology Information Security: Mobile security, cyber-physical systems, IoT security, security analytics, operational cybersecurity
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candidate must have experience in the following areas: molecular cell biology, plant phenotyping, and image/data analysis, as well as working as part of a large team. A track record of publishing research is
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Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate predictions Ability to work in a team Ability to communicate orally in
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considered. The successful candidate must have experience in the following areas: molecular cell biology, plant phenotyping, and image/data analysis, as well as working as part of a large team. A track record
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R or equivalent skills in another relevant language. We are not expecting you to be an expert in all forms of computer simulation, Large Language Models, or machine learning etc, but a working
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evaluation of North Atlantic jet-stream changes in large numbers of state-of-the-science global climate model simulations that have recently been produced by key inter/national projects, using the latest
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to analyse datasets Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models
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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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challenges of our time, with rising ideological divides and fragmented information ecosystems coinciding with increasing stress, anxiety, and declining well-being. Polarizing online content not only fuels