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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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2025. Encouraged by the continuing success of modern machine learning (ML) techniques, researchers have become ambitious to develop ML solutions for challenging science and engineering problems with
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Department/Location: Department of Engineering, Central Cambridge A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on experimental investigations
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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on experimental investigations of MILD combustion. The post holder will be located in Central
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to receive a first or upper-second class honours degree in Materials Science, Mechanical Engineering, Physics, or a similar discipline. A postgraduate master’s degree is not required but may be an advantage. A
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rolling basis, so apply early for the best opportunity to be considered. How To Apply Apply online by clicking the 'Apply' button, above. Select programme type (Research), 2025/26, Faculty of Engineering
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larger effort to map material performance limits and unlock untapped robustness in engineering alloys. You will: Develop and implement physics-based microstructural models to simulate damage and fatigue
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will have or expect at least a II(i) Undergraduate honours degree or Masters degree (or equivalent) in Materials Science, Chemical Engineering, Chemistry, Physics or related discipline. A background in
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possess or expect to obtain, a 2:1 or first-class degree in Engineering, Chemistry, or related physical sciences related discipline. To apply: send covering letter, CV and academic transcripts by email to
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Quantum Computing Centre (NQCC), Harwell, Oxfordshire Over the course of the studentship students will be offered a minimum of three months to work at the NQCC to apply their research within the national