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wave power without emissions. In the PhD you will have the opportunity to explore and develop novel renewable energy harvesting systems and devise and develop new modelling approaches and conduct
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. There is good theoretical and observational evidence that the accretion disc will likely be misaligned with the spin axis of the black hole although this is presently hard to pin down due to a lack of models
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memorisation capabilities of deep learning models. Such vulnerabilities expose FL systems to various privacy attacks, making the study of privacy in distributed settings increasingly complex and vital
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be used effectively as a performance digital twin to generate high-quality engine performance models and produce required training data for the proposed project. This could be a good starting point for
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, embrittlement, and cracks. This will be achieved by integrating ultrasonic arrays with inverse modelling methods to interpret historical data. Additionally, the project will explore the failure mechanisms
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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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-class facilities, enhancing their skills in materials characterisation, computational modelling, and experimental testing. These experiences will position the graduate as an innovator, ready to
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mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional
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unbounded variable and instance sets. In addition, novel approaches such as Physics Informed/Guided Learning allows the learning models to capture the underlying physics/patterns and to generate physically
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Mohamed Pourkashanian, Prof Lin Ma, Dr Kevin Hughes, Prof Derek Ingham Application Deadline: Applications accepted all year round Details This project will investigate the most efficient modelling