20 assistant-professor-computer-science-data Postdoctoral positions at University of Oxford in United Kingdom
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data from a variety of sources, including Spatial Transcriptomics and multiplex Spatial Proteomics platforms and developing skills in computational biology and mathematical spatial analysis via
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type (iv) work with the computational biology team to transfer this information into a AI algorithm that can distinguish neurodegenerative and neuroprotective phenotypes (v) work with colleagues in
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in Python, or demonstrated ability to rapidly acquire fluent knowledge of new programming languages, libraries, and platforms. A background and/or interest in mathematics or computer science would be
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projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
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robust deep neural networks applied to computer vision tasks. You should have the ability to manage your own academic research and associated activities. Informal enquiries may be addressed to Professor
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on academic research that relies extensively on survey data and work with interview data, historical case studies, cross-national and sub-national data, and the literature related to this programme of research
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Computational Methods for Advanced Research to Transform Biomedicine ( SMARTbiomed ), an international collaboration that integrates large-scale, multimodal biomedical data with advances in statistical and
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). Analysis will be performed on collaborative cloud computing environments using the extensive computing infrastructure developed by the Sanders Lab. You will be involved with all aspects of the data analysis
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We are seeking a full time Postdoctoral Research Assistant in Safe Machine Learning for Power Systems to join the Foerester Lab for AI Research (FLAIR) at the Department of Engineering Science
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autonomy and therefore is expected to show evidence of ability to conduct an independent research programme. Their research may be theoretical, numerical, or data-driven, or, ideally, a combination of the