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. The successful candidate will develop advanced skills in multi-modal sensor fusion, signal processing, machine learning, and integrity assessment, as well as transferable abilities in critical thinking, project
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of computational optimisation, machine learning, and experimental design of new chemistry. You will develop Bayesian optimisation and other active learning approaches for experimental design with a focus on real
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continuous programme improvement. Participate in educational initiatives and activities to enhance student learning outcomes. Requirements: A PhD or a Master’s degree (with significant industry experience) in
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: Erlangen Programme for AI” This is a 5-year programme supported by the EPSRC and is a collaboration of mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead
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Vision, Robotics, Evolutionary Computation, Deep Reinforcement Learning, and Machine Learning. This should include a proven publication track record. You should also have: Research Associate: A PhD (or
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of subjects such as computer vision, Machine Learning, Artificial Intelligence and Kinematics and dynamics. Autonomous systems, Robotics and Automation. Industry 4.0 and Internet-of-Things. Advantageous to have
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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are these impacted by different land use scenarios? How can we use machine learning to predict soil microbiomes from either pristine or agriculturally used soils, predict their functional capacity (e.g. carbon storage
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vision, robotics, machine learning and natural language processing (CSRankings) and were 7th in the UK for REF2021 outputs in Computer Science research. CVSSP and Computer Science are at the core of the
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability