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bioinformatics/computer science will be essential. Prior experience with connectomics data is highly desirable. Our group has developed an international reputation in this area and our tools have now been used in
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on Experimental Study and Computational Solids Mechanics model of soft multifunctional materials such as magneto-active polymers (MAPs), Electro-active polymers (EAPs). The successful candidate will work on the
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. Computing time is available on our local cluster and on the Vienna Scientific Cluster (VSC), a supercomputer shared by Austria's major universities. We focus on the development of methods to solve the many
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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Institute and Centre Lead for Cancer Genomics and Computational Biology at Barts Cancer Institute, Queen Mary University of London). We welcome applications from all qualified candidates. Research staff
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Institute and Centre Lead for Cancer Genomics and Computational Biology at Barts Cancer Institute, Queen Mary University of London). We welcome applications from all qualified candidates. Research staff
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will have or be close to the completion of a PhD in Neuroscience, Psychology or a closely related discipline. With in-depth knowledge of cognitive and computational neuroscience including motivation
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Metabolism (OCDEM) on studies related to circadian rhythms in population health. This post is part of a large, interdisciplinary research programme, offering attractive opportunities to work across
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emphasis on research infrastructure and technology rather than preparation for an academic career path. You will be involved in research, but more focused on learning and improving how computing, workflows
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-generation AI hardware (ASIC) accelerators. The UK's Advanced Research Invention Agency (ARIA) is supporting an ambitious programme of work that aims to reduce the the cost of AI by more than 1000x: https