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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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, 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
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polarisation shaping, 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
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-reviewed publications and project reports. Spanish language skills are desirable or willingness to learn. You should be willing and able to undertake extended international fieldwork and work across cultural
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, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques for generating high-resolution climate projections. In addition to developing