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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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cytometry will be an advantage. The project has a major computational component both for AI-driven modelling and predictions, and for bioinformatics analyses of wet-lab data. This will be performed by
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or study coordination, or at least active involvement in human studies during your doctoral studies Advanced knowledge in statistics and scientific publishing Experience with methods such as BIA
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able to demonstrate sufficient knowledge in the discipline and of research methods and techniques to work within established research programmes. Applicants will be able to demonstrate skills and
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of new contexts and methods of education in late antiquity. The postholder will also be expected to co-organise and take part in project-related events and activities. About you Applicants will possess a
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of techniques that encompass microbiology, biochemistry, metabonomics, bioinformatics and gut modelling. It will require a skilled postdoctoral scientist who will be familiar with such methods or is
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of research methods and techniques to work within established research programmes. Applicants will be able to demonstrate experience conducting field-based ecological or environmental science research
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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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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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at the Yusuf Hamied Department of Chemistry, University of Cambridge to work on the BBSRC grant "A Platform for Identifying GlycoRNA and Identifying Biases in RNA Pulldown". The role is to develop methods