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applicants with expertise in the fields of artificial intelligence, computer vision, edge computing, digital twins, human-computer interaction, user modelling, robotics, resilient computing, where
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of a catalogue of material composition of satellites 2. Developing material degradation models and estimating the material properties based on life and degradation. 3. Developing machine learning
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within their role. Person Specification Applicants must demonstrate research excellence in the field of Power Electronics, Machines and Drives, with the ability to teach our students to an exceptional
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research, delivering tutorials or computer practical classes, and possibly some lecturing. Allocation of teaching duties will take account of the Fellow’s expertise and experience as well as the Department’s
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in one or more of the Control Engineering specialisations including, but not limited to, Systems Modelling and Prediction for Electronics and Communications, Machine / Deep Learning for / in Control
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of advanced energy storage solutions and integration into the smart grid ecosystem. Implementation of machine learning techniques for predictive maintenance and fault detection in power systems. Exploration
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four (or equivalent) including English Language and Mathematics (or equivalent experience). 4. Post-16 qualification or equivalent experience. 5. Basic knowledge of computer programming, E,g., C
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computing, cloud computing, machine learning, artificial intelligence, programmable networks, digital twins, and cyber physical systems. Skills Demonstrable ability to work cooperatively as part of a team
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, an AI Assistant developed by Student Recruitment and Admissions with colleagues in Computer and Information Services and an external company (ICS.AI) was shortlisted for the Times Higher Technological
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-Performance Computer System, Hamilton. The appointee will be expected to cultivate an internationally-leading research group and to engage in the full Departmental range of teaching at both undergraduate and