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successes and proposes intelligent sensing and control solutions for automated robotic systems capable to be tele-operated using smart human-machine interfaces. This is an exciting PhD project that has a
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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on large annotated datasets. Memory-efficient deep learning: Model compression, pruning, quantisation, selective memory replay, and efficient training strategies. Energy-efficient deep learning: Methods
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, delivering high-resolution imaging and molecular Raman sensing to improve diagnosis of cancer and enable localised treatment. What we offer: The chance to work in a world-class multi-disciplinary team
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to co-design these avatars and training experiences. The goal is to create digital tools that help new or incoming carers feel better prepared for the specific behaviours, communication styles, and
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’ families, and community organisations to co-design these avatars and training experiences. The goal is to create digital tools that help new or incoming carers feel better prepared for the specific
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novel computational imaging and sensing techniques for compact imaging systems. These systems are applicable to all sectors which require compact imaging specifications, but will have a primary focus on
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the next generation of robots and its sensing solutions to perform tasks in challenging working environments. This project is related to the development of smart mechanisms and sensing to support the