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will develop the materials, methods, and designs necessary to 3D-print never-before created devices. You will develop new functional materials, and new engineering solutions only possible through multi
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offer their own ideas. Applicants who are skilled and knowledgeable in the areas of safety critical engineering research applied to multiphase thermofluids, materials and mechanical testing methods, and
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
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skills include: Interest or background in composite materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a
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programme. To acquire, analyse, interpret and evaluate research findings/data using approaches, techniques, models and methods selected or developed for the purpose. To establish a national reputation and
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Applications are sought for a fully-funded 42 month PhD studentship to work with Dr Rachel Nicks and Prof Stephen Coombes on the project: White Matter Computation: Utilising axonal delays to sculpt
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colleagues within the School and beyond, in projects at the interface of statistics, numerical analysis, scientific computation and the mathematics of machine learning. We welcome applications from excellent
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engineering. Expertise in numerical tools (Ansys, JMAG, .etc) and programming are desirable. Experience in electrical machine prototype development would be advantageous. Eligibility and Application
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technologies are critical enabling components for a wide range of QT based applications including quantum sensing, timing and computing To achieve this your role will involve developing inverse methods and
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, or related disciplines. Skills in numerical tools and programming are desirable. Any experience in engineering design or manufacturing would be advantageous. Eligibility and Application Due to funding