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We invite applications for a Postdoctoral Research Associate to work on an exciting new project to address significant mathematical and engineering challenges arising from multidimensional big data
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and educational contributors to early mathematical learning for neurodivergent children. Our focus will be three groups of children with genetic conditions diagnosed early in life (Down’s syndrome
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). The post is funded by BBSRC and is fixed-term for 18 months, expected to start on the 1 of November, 2024. You will be joining a team to carry out both experimental and mathematical modelling research
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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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to the development of relevant mathematical models, write articles and presentation summarizing results of investigations. You should possess a PhD/DPhil or be near completion (doctoral thesis must have been submitted
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the globally-distributed tskit-dev team. It is essential that you hold a PhD/ DPhil (or close to completion) in a quantitative science subject, together with experience in mathematical population genetics, in
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remotely). You will have or be close to the completion of a PhD or equivalent in computational sciences (Mathematics, Engineering, Computer Science, Statistics), together with relevant research experience
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, applied mathematics, physics, or computer science. You should have experience in developing and applying statistical methods and in programming. Experience in analysing high-dimensional datasets is highly
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strong quantitative background (e.g., computer science, mathematics, physics, engineering) is a requirement. The PDRA will also be expected to assist in the production of published material, and to make
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imaging data and identify & troubleshoot technical or scientific problems. You will have a Doctoral (PhD) degree or equivalent, or be near completion, in Biostatistics, Mathematics, Statistics, Statistical