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) and long-term properties (including creep and fatigue behaviour) will be analysed. Furthermore, the project will develop physics-assisted artificial intelligence (AI) models that integrate experimental
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the Centre of Excellence in Coatings and Surface Engineering (CE-CSE) at the University of Nottingham, funded by the UK government. There is a critical need to develop materials and coatings that can
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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), where heat is stored/released through reversible chemical reactions. This project focuses on NaOH water TCES systems, which use cheap, abundant materials [1]. We will develop modelling tools that combine
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the Faculty of Engineering, plus those housed in Plant Biosciences at the Sutton Bonnington campus. Data sets will be generated using simulated and experimental data and these will be used to train networks
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cell failure and its implications for system-level battery design. By creating a new scientifically robust framework to assess and mitigate thermal runaway, this PhD project will develop a unique
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modelling. You will be encouraged to develop and test your own ideas and hypotheses. There will be opportunities to attend training courses and summer schools, present your work at national and international
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Jaguar Land Rover and WMG (University of Warwick). The collaboration seeks to investigate and develop new joining methods of battery pack module assemblies. The biggest challenge is moving away from
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, and industrial deployment. You will develop and optimise scalable MEC systems for real industrial wastewater, focusing on process intensification, dynamic operation, and resource recovery. The project