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from end-of-life recycled aluminium to reduce embedded carbon level to below 2 tonnes CO2e/tonne of aluminium as a supplied component and eventually to Net Zero carbon. This project is concerned with
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to the complexity of the mathematical models that describe them. The current consensus is that there are three “types” of viscoelastic chaos: modified Newtonian turbulence, elastic turbulence, and elasto-inertial
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can be adjusted upon agreement with the successful candidate). Project Overview The drive for net-zero and sustainable manufacturing is reshaping the future of advanced materials. Traditional composite
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categories for a better capability of managing the uncertainty related to system complexity and data availability to achieve more accurate RUL estimations The student will have the opportunity to work with
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modelling capabilities for the prediction of energy extraction efficiency, especially focusing on improving the understanding and prediction of the complex flow phenomena, including buoyancy effects in AGS
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the scalability and robustness of AI in complex environments which is a major step towards the digital transformation of the manufacturing industry. Motivation Automation is key to meeting the growing demand
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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-processing crucial. However, video restoration and enhancement are complex due to information loss and the lack of ground truth data. This project addresses these issues innovatively. We propose using prior
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model of reaction barriers. This will enable the development of more accurate and advanced high-throughput reaction network discovery and by-product prediction. Background Typical drug molecules can
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net zero goals and the future of our planet. During their lifetime, those energy storage systems can experience complex electrochemical-thermomechanical phenomena that can result in their volumetric