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Field
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building typologies. This research aims to transform Pulse testing through AI integration—specifically leveraging descriptive, predictive, and generative modelling techniques—to enhance test accuracy
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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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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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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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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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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle
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complimentary computational studies to predict the intake aerodynamic characteristics and aid in the experiment design. This position is part of the CDT in Net Zero Aviation, which offers a modular, cohort-based