19 bayesian-estimation Postdoctoral research jobs at University of Oxford in United Kingdom
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About the role We are seeking a full-time Postdoctoral Researcher to join the Oxford Secure and Advanced Computer Architecture Research (OSCAR) group at the Department of Engineering Science
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energy densities exceeding LMFP and competitive with NMC. A postdoctoral research position is available on this 3D-CAT project in the area of computer modelling and materials design of lithium battery
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funded by UKRI EPSRC and is fixed term for 12 months. You will be contributing to joint UKRI EPSRC – NSF CBET project on sustainable computer networks, with a focus on carbon emissions reduction and
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic
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socioeconomic inequality. DIVREP involves three broad objectives: (1) assembling data on childbearing age and fertility levels, and estimating novel indicators of variation in reproduction for over 120 countries
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to adel.bibi@eng.ox.ac.uk For more information about working at
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely
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Genetics. These residential courses run over eleven months, with two terms of scheduled teaching and one term dedicated to hands-on laboratory or computer-based research. In the MSc in Clinical Embryology
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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary