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innovative research and technology transfer. The ability and willingness to adapt and incorporate novel ideas and approaches in deep-tech industry. Ability to initiate collaboration research in
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power systems, and/or plasma/x-ray/gamma-ray diagnostics. Demonstrated experience in handling vacuum system, high voltage (15 kV), high current (200-300 kA) pulse power systems. Demonstrated experience in
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capability to conduct innovative research. The ability and willingness to adapt and incorporate novel ideas and approaches in scientific industry. Demonstrated ability to formulate hypothesis and design
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. Working effectively as part of a research team in the development and promotion of the research theme. Ability to carry out analyse, interpret and critically evaluate data. Ability to prepare journal papers
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, spatially resolved optical mapping, or multidimensional imaging and spectral analysis, is preferred. • Strong experimental, quantitative data-analysis, and problem-solving skills, with the ability
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). Main Duties Develop research objectives and proposals for own or joint research, with assistance of a mentor if required Bring the ability to invent new, or adapt existing, methodologies. Contribute
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motivated Research Associate/Fellow in Electrical Machines to join the University of Nottingham's Power Electronics, Machines and Control (PEMC) group — one of the UK's largest and most ambitious research
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multidisciplinary team of researchers including learning scientists, AI experts, and instructors. Candidates who demonstrate the ability to independently design and implement experiments related to these areas will
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related field. At least 1 year of relevant experience in machine learning and structural health monitoring. Good written and oral communication skills. Proficiency in ABAQUS and laboratory testing. Ability
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analytical skills; (d) the ability to work independently and collaboratively; and (e) proficiency in both written and spoken English; Preference will be given to those with experience in semiconductor