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Tandem Industry-Academy funded project https://www.vaikuttavuussaatio.fi/en/funded-projects/tandem-industry-academia-tia-seed-2024/ . developing next-generation optical computing technologies for ultra
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(transient loads up to ~1 GW/m²). Computational modelling tools will complement the experimental work to guide joining strategies and post-weld heat treatment protocols. The outcomes will support selection
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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that simulates real-world environmental conditions to test the durability and longevity of these materials and products made thereof, is also required. This PhD project aims to investigate novel high-performance
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in the USA. The main project for the student will be the computation of the properties of hadrons using lattice QCD and high performance computers. The PhD student will further investigate the use
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the name behind sustainable, high-performance ingredients and technologies in some of the world’s most successful brands: creating, making and selling speciality chemicals that are relied on by
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PhD Studentship: Optimisation of Liquid Metal Filtration and Cleanliness in Nickel Based Superalloys
A four year PhD with integrated studies is available in the High Temperature Research Centre, School of Metallurgy and Materials under the supervision of Prof Nick Green and Prof Roger Reed, with a
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Applications are invited for a Research Assistant to join the Integrated Clinical-Computational Affective Research Unit (ICARUS). The unit is led by Dr Liam Mason (Associate Professor of Clinical
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processing conditions (e.g. aging and thermo-mechanical processing route) to optimise corrosion performance of Constellium’s high strength alloys. This project is an exciting opportunity to work in close
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physics and diagnostics at the York Plasma Institute Computational combustion modelling using High-Performance Computing (HPC) Machine learning techniques for predictive combustion models Research