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for an enthusiastic and highly motivated Research Associate or Research Assistant in Medical Statistics to join an interdisciplinary team focused on improving lives through world-class research in ageing, sarcopenia
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and enthusiastic researcher to develop their career in statistical epidemiology within the internationally leading Department of Statistics in the University of Oxford at a critical time for this field
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and enthusiastic researcher to develop their career in statistical epidemiology within the internationally leading Department of Statistics in the University of Oxford at a critical time for this field
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algebra, algebraic groups, statistical mechanics, topological quantum field theory, topological quantum computing, algebraic computation, and geometric topology. For more information on the programme
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or other cohorts. The successful applicant will use their computational and statistical expertise to identify novel research opportunities to better understand complex diseases. Main Duties : Develop
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, Computer Science, or related discipline. The successful candidate will possess a strong background in Biology, Statistics/Mathematics and/or Computer Science. Extensive experience in software and algorithm
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for statistical analysis together with experience of analysing real world data. Good track record of peer reviewed scientific publications, excellent team working and communication skills are also essential
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of Alcohol Research and the Liverpool University Hospital Foundation NHS Trust. You will have expertise in health data, data science, statistical modelling and/ or artificial intelligence. You will be based in
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, statistics or relevant subject area such as molecular biology. You should have strong computational skills and have past experience working with RNA and small compound data sets. Critical thinking, good
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Therapeutics in collaboration with the Liverpool Centre of Alcohol Research and the Liverpool University Hospital Foundation NHS Trust. You will have expertise in health data, data science, statistical modelling