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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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will be highly collaborative, and you will have the opportunity to learn from our highly supportive and skilled research group. The project will be primarily supervised by Prof Laurie King
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biophysical research and collaborating with a talented team, we’d love to hear from you! For more information about these positions, or to apply, please contact Prof. Nynke Dekker, e-mail: nynke.dekker
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electronic converters are required to connect renewable energy sources and energy storage systems to the power network. These converters employ sophisticated control algorithms that must simultaneously achieve
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative
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autonomous by embedding machine learning algorithms to search through different reaction parameters Person Specification Candidates should have been awarded, or expect to achieve, EITHER: A Bachelors degree in
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date: 29 September 2025 Award duration: 4 years Application closing date: Friday 2nd May 2025 or until the position is filled whichever is the soonest. Sponsor: EPSRC & HR Wallingford Supervisors: Prof
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transcripts should be sent to Prof. Zhirong Liao. Suitable applicants will be interviewed, and if successful, invited to make a formal application. Do not submit your application via the My Nottingham platform
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and academic transcripts should be sent to Prof Dragos Axinte. Suitable applicants will be interviewed, and if successful, invited to make a formal application. Do not submit your application via the My
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in