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non-reacting-flow simulations using lower-fidelity numerical models for compressible flows (Euler and URANS) will be compared to high-fidelity non-reacting-flow LES results to evaluate the predictive
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computing, dynamical systems, and applied mathematics to develop new mathematical frameworks for representing, compressing, and computing with complex physical systems. The project is carried out in close
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control functions; platform data handling functions related to security, data authentication, encryption/decryption and compression; the use of microelectronics devices, including COTS, in the development
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for representing, compressing, and computing with complex physical systems. The project is carried out in close collaboration with researchers in Electrical Engineering working on emerging memory architectures and
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the simulator as a supporting tool, you will evaluate the use of onboard AI and edge-computing architectures — including in-space data centres and AI-driven processing pipelines — capable of compressing and pre
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and reinforcement machine learning, model compression, transfer learning, natural language processing, large language models and trustworthiness (explainable Al, uncertainty quantification and surrogate