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Senior Postdoctoral Researcher: statistical and computational methods for complex traits in biobanks
, using genetics theory, computer simulation studies and analysis of data from large biobanks. To be considered, you must hold a relevant PhD/Dphil in a relevant field e.g., statistical genetics
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. The laboratory uses a combination of statistical, computational, and experimental approaches to understand how processes in our cells lead to changes in their DNA. These changes include errors (mutations
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. You will conduct statistical analyses of genetically informative data sets measured for phenotypes of direct or indirect relevance to psychiatry. You will have or be close to the completion of a PhD in
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collaborations across the University, and supervise research students. The successful applicant will also have the opportunity to conduct a small amount of teaching at the Department of Statistics in their areas
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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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technical background in mathematics and statistics, including Bayesian modelling is essential. You will also have demonstratable experience in Deep Neural Network based approaches, coding skills in Python and
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, PhD students, and/or project volunteers. It is essential that you have a PhD in Psychology (awarded or submitted/near completion) and have excellent statistical and data management skills, including
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of deep learning, drug design and engineering biology. Your skills and attributes for success: Strong track record in machine learning or statistical learning applied to biology, preferably in protein
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serve both as high-dimensional generalisations for well-known phenomena in random graphs and stochastic geometry, and as means to develop insight into statistical and computational challenges in