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Field
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at the Edge (CAITE) whose aim is twofold. First, it aims to bring AI at the edge, by providing scalability and resource efficiency through the development of cooperative, distributed AI algorithms, optimising
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platforms. Our group develops advanced algorithms and data analysis methods to address fundamental scientific challenges, including global cloud distributions, cloud microphysical properties and processes
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technologies (LCTs, such as solar PV, electric vehicles, heat pumps or energy storage) to distribution networks. New active technologies that provide flexibility, such as network operator-owned power electronic
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imaging. Develop, adapt, and evaluate modern reconstruction algorithms for accelerated and robust qMRI. Collaborate with a multidisciplinary team to translate methods to clinical research studies and
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imaging. Develop, adapt, and evaluate modern reconstruction algorithms for accelerated and robust qMRI. Collaborate with a multidisciplinary team to translate methods to clinical research studies and
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for in-space manufacturing and testing. Develop AI algorithms for navigation, guidance, and control. Lead collaborative research proposals for NSF, DoD, DOE, NASA. Mentor students and build
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experience in radar research, developing signal processing algorithms for long-range ultra-broadband Synthetic Aperture Radar systems and short-range FMCW systems. In recent years, breakthroughs in
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communication with a record of leading and reporting results. Desired Qualifications: Knowledge of quantum computing algorithms. Familiarity with tensor network methods. Experience programming GPUs. Experience
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-intensive, often being non-interpretable, and being highly brittle towards shifts in data distribution and changes in the problem domain. This fully-funded three-year project, with the possible further
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geometry and variable stiffness composites Implement bespoke optimization algorithms combining shell geometry and fibre distribution accounting for nonlinearity and uncertainty Conduct optimization studies