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Early-stage failure prediction in fusion materials using machine learning
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”. This exciting opportunity involves leading the development of advanced data-driven mathematical and computational models to suppress turbulence in pipe flows, contributing to pressing engineering efforts toward
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Machine tool dynamics-based digital twins for real-time monitoring of cutting tool conditions in smart manufacturing
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A Machine Learning Enabled Physical Layer for 6G Radio Systems
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Enhanced Remediation of Radionuclide Plumes through Chaotic Advection and Dynamic Pump-and-Treat Control School of Mechanical, Aerospace and Civil Engineering PhD Research Project Directly Funded UK
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Social invaders: Does sociality enhance the potential for successful invasion by alien species??
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High-speed astrophysics with ULTRACAM and HiPERCAM
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Mechanistic understanding, design and scale-up of Fluid-bed granulation School of Chemical, Materials and Biological Engineering PhD Research Project Directly Funded Students Worldwide Prof Rachel
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GIF CDT: A Novel Gas/Liquid contactor for Direct Air Capture and industrial CO2 capture technologies
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Forecasting the Future of Biodiversity: Cutting-Edge Approaches to Population and Community Dynamics