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machine learning, bioinformatics, sequencing and microbiology. This role is available on a fixed term basis until 31st May 2027. Hours of work are full time (36.25 hours). Job share arrangements may be
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takes input from a machine-learning platform (Python-based) to produce new compositions. A cobot delivers the material to the furnace or a plasma spray to produce new coatings. The proposed research is
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Revolution Industrialisation Centre at the University of Nottingham and contribute to the Power Electronics, Machines and Drives Research Group (PEMC). Your research will focus on electrical machines, drives
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Electronics, Machines and Drives Research Group (PEMC) at the University of Nottingham group and become a core member of a team researching power electronic converter design, control, construction and testing
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Researcher to be a part of the Power Electronics and Machines Centre (PEMC) at the University of Nottingham and become a core member of a team working on electrical machines, electromagnetic design, magnetic
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, regression, and computer visualization models should be highlighted in your application. Additionally, you will be expected to disseminate your research widely, support other post-graduate researchers, and
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group and become a core member of a team developing state-of-the-art electrical machines for future cars and aircraft. Applications are invited for the above position to join the growing Power Electronics
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/) and The Donald Danforth Plant Science Centre (https://www.danforthcenter.org/) The candidate should have (or nearly completed) a PhD in a computer vision or a deep learning-related subject. The ability
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Research (https://www.rothamsted.ac.uk/) and the Earlham Institute (https://www.earlham.ac.uk/). The candidate should have (or nearly have completed) a PhD in a computer vision or a deep learning-related
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thermodynamics; large-deviation theory; statistical mechanics of machine learning. The main responsibility of the role is to carry out theoretical and/or computational research in quantum and/or classical