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an available option. Applicants with a range of academic subject backgrounds are welcomed, including natural sciences, epidemiology, engineering, statistics and applied mathematics with experience and
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the
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analysis of data from a Nipah virus vaccine trial, using machine learning and statistical tools to identify immune response markers for future trials. You will be responsible for developing new and adapting
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available option. Applicants with a range of academic subject backgrounds are welcomed, including natural sciences, engineering, statistics and applied mathematics with experience and/or growing interest in
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. Proficiency in the use of statistical programming languages and analysis of large datasets and strong publication records would be essential. Previous experience in atmospheric dynamics and predictability is
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electronic health records (EHRs) from multiple UK hospital centres using advanced data analytics including machine learning, deep learning, and statistical techniques—with a particular emphasis on deep
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods