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, the process of recovering real-space images remains unclear due to the inherent and currently intractable complexity of deep learning methods. In this project you will develop Physics-Aware Super-Resolution
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This project could be right for you if you enjoy solid mechanics and structures, have an inclination toward computational and analytical mechanics, and are also interested in carrying out physical experiments
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will be to shortcut the current search process in classify crystallographic orientation. This will be built upon where machine learning algorithms will be developed to extract material elasticity
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laser ultrasonic imaging techniques with advanced artificial intelligence, the aim of this project will be to shortcut the current search process in classify crystallographic orientation. This will be
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suitable for applicants with a high level degree in Physics, Chemistry or Materials Science, ideally with some prior experience in quantum chemistry or solid state electronic structure calculations
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the experience of a PhD in ECS here . We welcome applicants from a range of fields including but not restricted to physics, engineering, electronics and chemistry. Entry Requirements A very good
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awarded a first class or 2.1 honours degree (or equivalent if from other EU countries) in materials science, chemistry, physics, chemical engineering, mechanical engineering, manufacturing, or a relevant
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facilities at the University of Sheffield, using cutting-edge techniques for water quality analysis including online physical/chemical parameter monitors and advanced microbiological analysis, and will become
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Engineering, Physical Science, Mathematics, Computer Sciences, Physical Geography or a related subject. • Good level of written and oral communication skills, as appropriate for disseminating research and
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that are submitted without following the correct application process, or those exceeding the page limits for CV’s and motivation letters will not be considered. The successful applicant will subsequently be required