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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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Models (LLMs). Orchestrating AI/ML pipelines in 6G. Developing certification and checking processes for code inside ORAN 6G. The research will be a combination of software engineering, radio
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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-engine aerodynamics. The project is aligned with the acknowledged skills development needs in the areas of aircraft/propulsion integration, aerodynamics, modelling, software development, research testing
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matter-PFAS-adsorbent interactions which in turn will have the potential to change our understanding of PFAS removal mechanisms. Using a combination of real and model water sources, experiments will
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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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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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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