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in the groups of Dr Florence Hardy and Prof Anthony Green, University of Manchester, as part of the cross-institutional BioAID Doctoral Training Programme, including world-leading experts from Queen's
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this challenge head on by combining quantum-mechanical calculations with state-of-the-art machine learning (ML) methodologies to explore and optimise the compositional space of complex high-entropy metal oxides
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
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19 Jan 2026 Job Information Organisation/Company The University of Manchester Department Computer Science Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD
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Jyvaskyla University of Manchester Kone Oyi The candidates will have the opportunity to visit various partners in the network, supported by a mobility allowance. At the Chair of Machine Learning for Complex
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, alongside their application to challenging chemical problems. The group combines pulse sequence design, experimental NMR, and computational approaches, including modelling, AI, and machine learning
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candidate will benefit from training facilities in The Manchester Metropolitan University and The Dermatology Centre, University of Manchester, Salford Royal NHS Foundation Trust. Project aims and objectives
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The student will benefit from working alongside a multidisciplinary team of engineers, mathematicians, and physicists at the University of Manchester as well as a wide collaboration network within the UK and