55 computational-model Postdoctoral positions at University of Oxford in United Kingdom
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SECURE project, a major multi-partner programme developing self adaptive gene therapies for neurological disease. This will involve (1) producing a single nuclei atlas of the substantia nigra and (2
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between multiple organoid models. You will contribute intellectually to the development of the research and help shape the wider research programme. Responsibilities will also include helping with
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practice and knowledge of meditation traditions. You have a proven track record in interdisciplinary research, combining computational modelling, whole-brain fMRI analyses, altered states of consciousness
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. The post-holder will be one of six centre-funded postdoctoral researchers delivering on projects that form our core research programme. They will be a cornerstone of the centre, collaborating across our
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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interdisciplinary research programme investigating how immune mechanisms contribute to psychiatric and neurological disorders. The project combines human induced pluripotent stem cell (iPSC)- derived neuronal and
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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting-edge deep generative probabilistic models
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, Computational Astrophysics, Early Universe or Gravitational Physics will be an advantage as well as experience in the analysis or modelling of LSS data, including (but not limited to) galaxy clustering and CMB
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Oxford Martin Programme on Decarceration, and the Forensic Psychiatry research group at the Department of Psychiatry working independently to carry out risk prediction modelling and epidemiology related
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly