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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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science/engineering, applied physics/mathematics, or related fields. Prior experience in computer vision would be beneficial but not essential; determination, curiosity, and a willingness to learn are key attributes we
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Rolls-Royce, this project will use both experimental and computational aspects to explore the aerodynamic design space for coupled intake/fan configurations that are required to deliver more efficient
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of
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simulating fluid networks and dynamic phenomena for assessing different solutions is a necessity The overall aim of this project is to improve the confidence in fuel system design process for ultra-efficient
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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
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fitting for reduced order electrochemical models. Early detection of thermal anomalies in battery packs. Physics-based models and state of health estimation in lithium-sulfur batteries. Collecting data and
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, in collaboration with Rolls-Royce, will develop innovative coatings to safely contain hydrogen in critical aerospace materials through experimental and computational modelling work. You’ll join a
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, computational modelling and experimental work. You’ll join a pioneering multidisciplinary team that values equity, diversity, and inclusion, gaining unique expertise in turbomachinery pump development, hydrogen