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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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experience would likely come from economics, physics, or other computer and mathematical sciences. You will possess sufficient specialist knowledge in the discipline to work within established research
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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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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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, statistics, mathematics, engineering or other relevant areas and have up-to-date knowledge in advanced AI topics, such as deep learning, representation learning, sequence models, NLP, multimodal AI, generative