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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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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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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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fuel, but its high reactivity makes it vulnerable to pre-ignition. The presence of lubricating oil droplets can worsen this risk by evaporating, altering chemical pathways, and producing nanoscale soot
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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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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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well as cutting-edge research insight. Description The global drive towards electrification in high-performance sectors such as motorsport and aerospace is pushing electric motors to operate at ever increasing
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environment. Accurately predicting flow and heat transfer in these systems is critical for safety, performance, and design assessments, yet direct high-fidelity simulations, such as Large Eddy Simulation (LES
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine