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estimation for battery management systems for lightweight lithium-sulfur batteries and have specialist expertise in modelling, control and estimation theory, system identification and computer
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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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of the start of the PhD Award). A demonstrated background in communication theory, networking, and AI would be a distinct advantage. Funding This studentship is open to UK and international students
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with a background in mechanical, aeronautical, automotive, civil / industrial and/or software engineering (or similar) and/or mathematics and/or physics. The ideal candidate will have a solid background
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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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high temperature corrosion rate involving mathematical models validated through simulation, experiments and analysis. Gas Turbines are used as a multipurpose power source in various applications like aviation, power
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Entry requirements Applicants should have a first or second-class UK honours degree or equivalent in a relevant discipline. This project would suit a student with engineering, physics, mathematics
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Applicants should have a first or second-class UK honours degree or equivalent in a relevant discipline such as engineering, physics and mathematics. Prior experience in fluid networks modelling is beneficial
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engineering, computer science/engineering, applied physics/mathematics, or related fields. Prior experience in computer vision would be beneficial but not essential; determination, curiosity, and a willingness
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience