589 parallel-and-distributed-computing-phd-"Multiple" Postdoctoral positions in United Kingdom
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, and the Tees to Topping Green Corridor running into and out of Middlesbrough. These three green corridors are part of an ambitious programme being led by the National Trust to help work in partnership
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schizophrenia-related symptoms in animal models (mice), in the context of a collaborative project with clinicians and computational scientists. This project will be supervised by Prof Oscar Marin and Prof Beatriz
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clinicians and computational scientists. This project will be supervised by Prof Oscar Marin and Prof Beatriz Rico. Candidates should have strong experience in stereotaxic surgeries and Electrophysiology
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interpersonal skills are required to ensure success in liaising with a large and diverse research team: PhD in Organic Chemistry or a related field. Strong background in synthetic organic chemistry, and/or solid
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to follow this initial post. Applicants should hold a PhD (or equivalent) involving neuroimaging, cognitive neuroscience or a related field. Experience in MEG/OPM-MEG acquisition and data analysis is
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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are looking for a highly motivated and enthusiastic candidate with a PhD in Physics, Chemistry, Materials Science, Engineering, Chemical Engineering or a related area. A proven track record of scientific
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methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine
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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