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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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that will inform teaching, and actively support recruitment and outreach activities, such as open day and school liaison events. You will teach across our suite of biological science degrees, focusing
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crystallography aspects of our research (predominantly in the area of crystalline materials), while also ensuring integration with the other research areas in the Cooper Group (i.e., robotic automation and machine
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following: Strong programming skills (preferably in Python), with experience in machine learning/AI or software engineering for interactive systems Desirable experience in game development or design using
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with experts to automate diagnostic assays, leading to cost-effective, easy to use tests Work closely with AMR, Informatic and Machine Learning colleagues ensure the tests provide accurate pathogen ID
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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undergraduate and/or postgraduate taught levels in Higher Education. This should be beyond the level of teaching assistant roles. You will offer innovative, inclusive learning and assessment approaches for in
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to the development of robust, reproducible machine learning workflows. The role forms part of a nationally significant research programme with strong policy relevance, working in collaboration with the Imago Data
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, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
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habitat establishment on rock armour affects performance under UK-relevant conditions. You will then develop a predictive Machine Learning tool to support engineers in incorporating vegetated systems