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. This role is ideally suited to a researcher with experience in computational structural biology, glycobiology, or protein modelling, who is interested in applying AI-enabled structural prediction and glycan
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this role, you will contribute to TOZCA’s whole‑aircraft noise prediction capability, ensuring acoustics are robustly represented within the project’s wider modelling framework. You will develop, apply and
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predictive modelling, classification approaches, pathway analysis, and topological data analysis. You will be based within the laboratory of Professor Paul Skipp, working closely with colleagues in the Glyco
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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on understanding the aerodynamics and aeroacoustics of overlapping propeller systems. This project will involve high-fidelity flow and noise measurements, combined with semi-analytical predictive models
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with semi-analytical predictive models, to establish new physical principles for designing high-efficiency, low-noise multi-rotor configurations. You will have access to state-of-the-art facilities