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Field
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. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess the structural performance of composite sleeves under operational conditions
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or joining thin-wall Titanium and Nickel alloys at high temperatures. Due to the unique material behaviours of these sheets and foils (0.1 mm to 0.5 mm thick), controlling variables in the forming process is
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season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
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of these sheets and foils (0.1 mm to 0.5 mm thick), controlling variables in the forming process is challenging. Characterising the mechanical behaviours of thin foils at elevated temperatures is crucial in
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, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
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expertise and facilities in electrochemistry, materials chemistry, advanced characterisation techniques (including a variety of spectroscopy, microscopy,) modelling and battery and fuel cell construction and
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mitigation strategies to prevent performance losses due to these impurities. We will explore both experimental techniques as well as computational models to provide feedback for designing higher efficient
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noise models, leading to metrics devoid of assumptions about noise impacts (e.g., cross-talk or non-Markovian noise in gate fidelities). As shown by the supervisory team, non-Markovian noise can be a
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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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(e.g., in the context of risk, uncertainty, future thinking), social processes (e.g., interpersonal dynamics, social norms, and influence), and behavioral outcomes (e.g., behavior change, social action