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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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and supporting PhD supervision in areas related to through-life engineering services, smart machines and autonomous systems. This position offers an excellent opportunity to work at the interface
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to contribute to the wider academic mission of the group, including supervision of MSc student projects and supporting PhD supervision in areas related to through-life engineering services, smart machines and
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academic mission of the group, including supervision of MSc student projects and supporting PhD supervision in areas related to through-life engineering services, smart machines and autonomous systems
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experience) in applied mathematics, physics or engineering, and strong, up-to-date specialist knowledge in analytical and numerical spray modelling and machine learning. Experience with OpenFOAM or similar CFD
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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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, software engineering, Embedded Systems, Artificial Intelligence / Machine Learning, data science, or closely related disciplines or significant practical/industrial experience. Significant postdoctoral
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during
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This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological prediction. The Research Fellow will develop ML methods for postprocessing numerical ensemble weather
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applications, and presenting research at local, national and international forums. PhD supervision and public engagement experience is also desirable. You will bring deep expertise in ML/DL techniques, NLP