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social care, and investigating the use of reasonable adjustments to reduce healthcare inequalities, and a novel Machine Learning workstream to develop an intervention which may be implemented in routine
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states by detailed characterisation of patients during diagnostic and therapeutic procedures through designing and conducting multicentre randomised clinical trials to applying machine learning and
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, we are looking for candidates to have the following skills and experience: Essential criteria PhD awarded (or near completion) in Electrical/Electronic or Computer Engineering. Strong publication
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or machine learning methods Advanced knowledge of electronic healthcare records and their use in development and validation of risk prediction models Knowledge in application of econometrics in research
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or Computer Engineering. Strong publication record in machine learning, including in top-tier machine-learning conferences and journals Experience in presenting research results and/or tutorials in top-tier
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following criteria Background in probabilistic machine learning Track record of high-quality research publications in peer reviewed conferences and journals. This is a full time post, and you will be offered
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experience: Essential criteria PhD awarded in Computer Science, Machine Learning, Biomedical Engineering, Mathematics, or a related subject area * First or Second-Class Honors in mathematics, physics
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To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD awarded in Computer Science, Machine Learning, Biomedical Engineering
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. To achieve these goals, we develop advanced machine learning methodologies, particularly weakly supervised learning approaches, to analyze and classify imaging and transcriptomics data. Our research emphasizes
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Experience in human-centred design or user experience (UX) research in digital health Understanding of machine learning, AI, or big data analytics applied to health apps Experience working on NHS-funded