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of polycrystalline metals. The post holder will be located in Central Cambridge, Cambridgeshire, UK. The key responsibilities and duties include setting up the framework; validating the results using theory and
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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Deployment The PhD programme offers: Training in the theory for solar energy technologies, experimental measurement and evaluation techniques, tools for modelling and predicting PV generation. Opportunities
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will be augmented with atomistic structure data from electronic structure theory and STEM image simulations. All data will be combined into an automated workflow that predicts thermodynamically stable
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analytical frameworks grounded in Mean Field Game (MFG) theory and Multi-Agent Reinforcement Learning (MARL), which are tailored for eCPS. These frameworks will facilitate the creation of effective control
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aforementioned tasks with the following actions: Develop the principles and theories for governing the scalability principles for building innovative robotics end-effectors that can access geometrically complex
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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
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estimation of useful life. In this research, a theory will be postulated for the combined mode of gear failures. The theory will be supported by the basic gear failure mathematics and preliminary validation