55 computational-model Postdoctoral positions at University of Oxford in United Kingdom
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institutions. You will lead the computational aspects of this exciting project by leveraging AI tools such as AlphaFold and developing novel deep generative models and/or large language models. The aim is to
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30 September 2028. This project is associated with an exciting new EPSRC/UKRI-funded Programme Grant entitled “Advanced Device Concepts for Next-Generation Photovoltaics.” This collaborative project
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that form our core research programme. They will be a cornerstone of the centre, collaborating across our three Research Pillars to generate hypotheses and data to underpin subsequent grant applications and
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collaborative programme bringing together a team of leading experts in advanced electron microscopy imaging, first-principles modelling, metal halide semiconductor thin-film and device fabrication, and
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out rigorous and impactful research into the computational mechanisms of human learning using deep neural network models, and disseminating the findings within the research group, across the wider
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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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Chris Holmes, the centre will initially focus on the following thematic areas: • Decision analysis under model misspecification • Uncertainty quantification around LLMs
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understanding of dark energy. Projects may span a broad range of topics, including improving Type Ia supernova modelling and standardization, developing and applying advanced data analysis and statistical methods
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training of mice, in vivo two-photon imaging, viral circuit tracing, and computational analysis to study how fronto-sensory feedback microcircuits contribute to adaptive cognition. The successful applicant
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researchers will extend and apply the ideas of active matter physics in biological contexts, developing theories and cell-scale and continuum computational models. The work will focus on identifying physical