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in implementing, testing and validating complex minimisation algorithms that can be used for adaptive trials. Application & interview 8 Experience of collaborating on successful research proposals
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expectation to contribute to scientific publications and demonstrations. Support will be provided by senior colleagues in the Digital Manufacturing Laboratory. You will have completed a First degree in Computer
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Programme: Hybrid CFD and process simulation for process intensification of post-combustion CO2 capture School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof
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will primarily support the Head of School (Professor George Panoutsos, Chair in Computational Intelligence) and his research activities in the area of Machine Learning (ML) for Engineering, focusing
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collaborate with researchers at Ludwig Maximilian University, Munich and Centre Algatech, Trebon, Czech Republic, as part of the Synergy research programme. The aims of the Sheffield part of the Synergy
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criteria in the application statement when you apply. Essential criteria Current student or graduate in Computer Science, Software Engineering or a related discipline (assessed at: application/interview
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/MSc or equivalent in Computer Science or Software Engineering (assessed at: application/interview). A track record in software testing, testing for autonomous systems, or autonomous driving system
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/MSc or equivalent in Computer Science or Software Engineering (assessed at: application/interview). A track record in software testing, testing for autonomous systems, or autonomous driving system
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programme. This is an exceptional opportunity to gain experience in medical device innovation, translational research, and clinical validation. The ideal candidate will have a background in biomedical or
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of the research programme as discussed with the fellowship supervisor. Contribute to research group planning. Coordinate and liaise with other members of the research group over work progress. Supervise research