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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
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on patient care through improved diagnostic pipelines, integrative analytics, and data‑driven insight. As fitting with a Research Fellow or Senior Research Fellow, you will: Drive innovative research
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discipline. Applications will also be considered from candidates who are working towards or nearing completion of a relevant PhD qualification. Applicants should have excellent experimental and analytical
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the mentorship of leading experts in one of the following priority research areas: Research area 1: Intelligent Structural Optimization using Physics-Informed Reinforcement Learning Research area 2: AI-Enhanced
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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
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-Informed Reinforcement Learning Research area 2: AI-Enhanced Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates
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, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre laser’, through
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or machine learning. Excellent programming skills in Python and deep learning frameworks A collaborative mindset and interest in socially impactful research. Experience with sign language data, multimodal
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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