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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
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phase locked loops. The group is a world leader and initiator of many new research directions which also find their application in industrial products. The group has a strong collaboration with the global
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predicting the snow performance of winter and all-season tread compounds. The key objectives include: Development of a laboratory method Achieving a snow performance prediction accuracy of 95% Ensuring high
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reinforcement and reductions in hysteresis will be evaluated. Objectives of project proposal: Enhance mechanical strength and modulus by incorporating lightweight reinforcing materials Improve cut and tear
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logistics companies to collaborate effectively and optimise operational scheduling across multi-actor systems, ensuring sustainability and efficiency at both company and system levels. Research Objectives
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application before June 30, 2025. We will fill this position as soon as we find the right candidate, so we encourage early applications. Your application must include: A cover letter (maximum 2 pages A4
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new research directions which also find their application in industrial products. The group has a strong collaboration with the global semiconductor industry. Are you interested in a PhD position at the