91 condition-monitoring-machine-learning-"Multiple" Postdoctoral positions at University of Oxford
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administrative tasks, this will include small scale project management to coordinate multiple aspects of work to meet deadlines. You will adapt existing and develop new scientific techniques and experimental
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atmosphere and main group chemistry. You will be expected to manage your own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects
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, co-ordinating multiple aspects of work to meet deadlines. The post will be based in the Kavli Institute for Nanoscience Discovery, New Biochemistry Building, South Parks Road, Oxford and is available
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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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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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manage your own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. You will adapt existing and develop
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across multiple leading institutions with international collaborators across 5 continents. The postholder will be involved in the development and implementation of novel behavioural and fMRI tasks and
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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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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
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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