72 condition-monitoring-machine-learning Postdoctoral positions at University of Oxford in Uk
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the volcanoes of the Eastern Caribbean as a focal point and, with our international partners, will demonstrate how this knowledge can improve monitoring and warning systems in the Eastern Caribbean. The
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The University of Oxford is seeking a highly motivated Postdoctoral Scientist with expertise in biostatistics, machine learning, and cardiac magnetic resonance imaging (MRI) to join Professor Betty
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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The post holder will develop computational models of learning processes in cortical networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity
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that control the response to low oxygen conditions in Marchantia polymorpha. They will contribute both to the practical work with plants but also some bioinformatics work on protein structure and function
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carboxylic acids. This will exploit the ability of the nickel-iron hydrogenase enzyme to activate hydrogen gas under mild conditions to unlock mild and selective hydrogenation and dehydrogenation processes
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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and motivated candidates for a postdoctoral positions working on cutting-edge research at the intersection of Machine Learning, Privacy-Enhancing Technologies, and Public Interest Technology. We
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing