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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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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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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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to also improve and scale the process. We have made major contributions in this area, including the use of Machine learning to discover new cryoprotectants [Nature Communications 2024, 15, 8082
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their applications. Using machine learning and related tools to enhance quantum memory advantages in stochastic simulation. Using advanced tensor network techniques to enhance the modelling of complex, memoryful open
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sluggish diffusion kinetics of HEAs make them excellent candidates for resisting oxidation and corrosion in high-temperature steam. Guided by thermodynamic modelling and machine learning, we will identify
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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