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platforms at both locations, providing the student with hands-on industrial experience as well as cutting-edge research insight. Description The global drive towards electrification in high-performance
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-box techniques in the industry is still high. One of the main reasons is that the performance of such techniques highly depends on a large amount of good-quality data. Unfortunately, the availability
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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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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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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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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
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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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Research Group at the Faculty of Engineering which conducts cutting edge research into experimental and computational heat and mass transfer, multiphase flows, thermal management, refrigeration, energy
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be used effectively as a performance digital twin to generate high-quality engine performance models and produce required training data for the proposed project. This could be a good starting point for
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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