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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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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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, 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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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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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
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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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turbomachinery for improved efficiency and compactness. This position would suit a researcher with a strong background in Computational Fluid Dynamics (CFD) and an interest in Chemistry modelling and Machine
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learning phase-picking models, combined with advanced phase association, probabilistic earthquake location, and relative relocation methods, to significantly enhance earthquake detection levels and location
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. At present there is specific interest in advanced 3D perception techniques such as geometric foundation models, implicit neural rendering (NeRF, Gaussian Splatting) as well as semantic mapping. Our research
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particular focus on helping build machine learning models that can help humans learn faster and more effectively, and/or make better decisions. They will independently manage their academic and associated