93 condition-monitoring-machine-learning-"Multiple" Postdoctoral positions at University of Oxford
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administrative activities under supervision from senior researchers. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. Responsibilities will include
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sleep; performing anatomical tract tracing; analysing existing and new datasets using python and Matlab using advanced statistical methods such as machine learning; collaborating with other members
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drug and disease research in multiple projects in the group. The candidate is expected to lead a drug development project and support other group projects. This will include lab experiments, analysing
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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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own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. The post will be based in the Department
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, to co-ordinate multiple aspects of work to meet deadlines. You will undertake laboratory work as required, such as sample preparation, cell culture, analysis of tumour samples and, tissue staining. Other
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close to completion of a relevant PhD. You will manage your own academic research, effectively coordinating multiple strands of work. Direct experience in molecular genetics and/or plant-microbe interactions
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interpretation of atmospheric circulation in high-resolution reanalysis data, idealised model simulations and a state-of-the-art weather forecasting system. The post-holder will have the opportunity to teach
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project management, to co-ordinate multiple aspects of work to meet deadlines. The post will be based in the Department of Chemistry, Chemistry Research Laboratory, 12 Mansfield Road, Oxford, OX1 3TA and is
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We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans and machine learning systems led by Prof Chris