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of large-scale robot systems (ground and aerial). The ideal candidate will possess hands-on experience with designing and implementing reinforcement learning algorithms, and deploying them onto real robots
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. The Role Applications are invited for a postdoctoral research associate in the theory of soft and active matter, to work with Prof. Suzanne Fielding in the Department of Physics at Durham University on a
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, estimation, and identification algorithms that directly interface with physical hardware. We work closely with industry partners. Our research has led to several methods now used in commercial products. We
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. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming, analysing and
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algorithms, and decision-support tool development. Responsibilities will include programming, analysing and interpreting data, and contributing to innovative solutions that support maritime decarbonisation
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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the coordination of large-scale robot systems (ground and aerial). The ideal candidate will possess hands-on experience with designing and implementing reinforcement learning algorithms, and deploying them onto real
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small highly motivated inter-disciplinary team working towards a shared goal. You will be responsible for the design and testing of original machine-learning based algorithms and models for multi-modal