708 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "https:" "CNRS " positions at University of Sheffield
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
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student will gain experience across different disciplines including engineering, and neuro-computation. We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit
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Conrad, N. (2020). Proofreading revisited: Interrogating assumptions about postsecondary student users of proofreading. Journal of English for Academic Purposes, 46, 100871. https://doi.org/10.1016/j/jeap
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) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution. Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/acse
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work on one or a number of these aspects. Students with good degrees on robotics, electrical engineering, computer science, mathematics, cognitive science or subjects where signal processing and
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molecular detail. You will use a combination of structural computational/artificial intelligence approaches, protein biochemistry and single-molecule techniques, such as optical tweezer assays and single
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Novel routes for in-situ measurements during the manufacture of thin flexible electronic films. School of Chemical, Materials and Biological Engineering PhD Research Project Self Funded Prof
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Constrained control and set invariance School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr P Trodden Application Deadline: Applications accepted all year round
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engineering, super resolution microscopy and intracellular transport assays. In addition, the student will also gain experience in generic approaches such as molecular biology, cell culture and viral
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Advanced Carbon Fibre Composites using Next-Generation Additive Manufacturing (CFAM) School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Patrick Fairclough