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of mole activity and soil health and biodiversity, collecting data on visitor perceptions of moles and their management, and analysing findings using statistical modelling approaches. The role provides
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in Cambridge. The mission statement of the group is "developing statistical methods to use genetic variation to answer clinically important questions about disease aetiology and prevention". The three
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science or a related field near completion* Previous experience in analysing neuroimaging (structural magnetic resonance imaging or low field neuroimaging) data across age-groups Advanced (statistical) data
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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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. Advanced (statistical) data analysis (including meta-analysis and longitudinal analysis) and programming skills Ability to write complex reports and/or scientific publications, demonstrated through emergent
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science or a related field near completion * Previous experience in analysing neuroimaging (structural magnetic resonance imaging or low field neuroimaging) data across age-groups Advanced (statistical
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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
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outstanding PhD in psychology, research experience in aging psychology and development in adulthood and excellent statistical methodological skills. You will have the opportunity to work in a highly motivated
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an initially solid-like state firsts yields and starts to flow, and in particular on the statistical physics of how initially sparse plastic events in an otherwise elastic background then spatio
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in Cambridge. The mission statement of the group is "developing statistical methods to use genetic variation to answer clinically important questions about disease aetiology and prevention". The three