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manufacture, in experimental techniques, characterisation and computational modelling. In particular, you will gain experience in electrode manufacturing techniques, electrochemical characterisation, particle
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either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution. This project will require strong skills in signal
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of the users (humans) and the physical and technological world. Both people and the physical world interact through networks, social and technological (e.g. the power grid). As our knowledge in science, society
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coatings on real-world materials. Gain expertise in material science, tribology, surface engineering and advanced manufacturing technologies. Make a tangible impact on productivity, tool longevity, and
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social science research in secure computing environments, and will make a significant contribution in advancing secure data infrastructure and federation across the UK. Main duties and responsibilities
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This is a self-funded research project. We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable
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have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Chris Race (christopher.race@sheffield.ac.uk). Application Webpage: https
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. This paves the way for the application of MPC to large-scale systems, since the computational bottleneck is removed. The basic challenge is how to coordinate the distributed decision making of agents so that
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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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performance. Research in the group is a mixture of experimental and computation work. A current key focus of the group is development of new multiscale modelling approaches, coupled with data driven modelling