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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science, applied mathematics, or a closely related field. Experience or interest in
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biomedical engineering). Flexible start date! What You’ll Need A first-class or upper second-class honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics. Experience in coding and CFD
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with possible supported secondment in company during the study. The student should have a strong interest in mathematical science, software development, data driven modelling, structural mechanics and
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science, engineering, mathematics, or related subject) Proficiency in English (both oral and written) Essential to have strong foundations in computer systems through degree courses or equivalent work experience
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involve collaboration with our industrial partners. Projects provide a unique research and training experience and an exciting opportunity for you to develop mathematical innovation inspired by contemporary
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Research Details sections of the form. Candidate requirements: Applicants must hold/achieve a minimum of a merit at master’s degree level (or international equivalent) in a science, mathematics or
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, engineers, and the general public Qualifications Research Assistant Degree in engineering or numerate subject (e.g., mathematics, physical sciences, computer science) PhD close to completion in field of Power
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience
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second class UK honours degree or equivalent in a related discipline, such as computer science, mathematics, or engineering. The candidate should be self-motivated and have excellent analytical, reporting
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manufacturing (year 1) - Advanced Composites manufacturing using energy absorbing fibres and nanomaterials (year 1) - Analytical/mathematical modelling and FEA modelling of hyper-velocity impact test of