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explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers the opportunity for the PhD student to lead the development of innovative simulation tools
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 1 month ago
skills applicable to geometry handling, variance reduction, data analysis, and radiation physics. Your main work tasks will be Radiation safety simulations for beamlines and accelerators. Design of
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platform as a doctoral student? At Fraunhofer IPMS , in collaboration with renowned German and European partners from science and industry, we are developing analog accelerators using novel non-volatile
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gross salary) for 3 years An additional £2,000 per annum for consumables and travel Requirements The candidate should have a 1st or high 2:1 degree in electrical/mechanical engineering, physics
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to greatly accelerate bioactive molecule discovery and significantly reduce costs in drug discovery, enabling new drug targets that are currently economically unfeasible such as in rare and poverty-related
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to greatly accelerate bioactive molecule discovery and significantly reduce costs in drug discovery, enabling new drug targets that are currently economically unfeasible such as in rare and poverty-related
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to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers
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platform as a doctoral student? At Fraunhofer IPMS , in collaboration with renowned German and European partners from science and industry, we are developing analog accelerators using novel non-volatile
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pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM