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systems. There are virtually no satisfactory ways of exhaustively ensuring and demonstrating that these stochastic systems meet the demonstrable, repeatable, and predictable expectations of existing safety
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future propulsion systems. There is also opportunity for successful candidate to collaborate with experimental teams for materials synthesis, characterisation and validation of computational predictions
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of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic
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prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic system
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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effective flow control strategies Develop ML models to predict complex flows in porous media configurations Design optimised porous media geometries for enhanced mixing efficiency. Training opportunities
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aircraft icing conditions. This data can then be utilised for improving design of ice detection and mitigation systems and for refining icing prediction codes. Unique opportunities for conference attendance
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improving the reliability of the prediction of structural performance. This project aims to continue developing the stochastic inference framework by leveraging recent advances in artificial intelligence
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environment. Accurately predicting flow and heat transfer in these systems is critical for safety, performance, and design assessments, yet direct high-fidelity simulations, such as Large Eddy Simulation (LES
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-based structural integrity model, validated using synchrotron X-ray microtomography and phase contrast imaging, to predict the lifetime of UK’s advanced gas-cooled reactors fuel cladding in storage