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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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-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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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