45 distributed-computing-associate-professor PhD positions at University of Nottingham
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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suitable for a hard-working researcher with an interest in respiratory infections. Essential skills: A BSc degree or equivalent ideally in a health related field, excellent computer literacy, good inter
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at the University of Nottingham. The PEMC group has undergone a significant period of growth and now has over 150 members, with 18 academics (including 7 full professors) and approximately 120 PhD students and post
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At the Mixed Reality Laboratory, School of Computer Science, University of Nottingham Closing date: 28th March 2025, interviews will take place in April and May Department: Computer
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challenges associated with its conversion are overcome. This PhD project aims to investigate and optimise contemporary enzymatic routes for the conversion of lignocellulosic waste feedstocks from local
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suitable for a hard-working researcher with an interest in respiratory infections. Essential skills: A BSc degree or equivalent ideally in a health related field, excellent computer literacy, good inter
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3-year PhD studentship: Scaling-Up Functional 3D Printing of Devices and Structures Supervisors: Professor Richard Hague1 , Professor Chris Tuck1 , Dr Geoffrey Rivers1 (1 Faculty of Engineering) PhD
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PhD Studentship: A continual learning approach for the development of robust robotic control systems
. AI is expected to be at the centre of these systems, being the foundation of computer vision, monitoring, and control solutions. Despite the promising results that AI (and especially Deep Learning) has
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candidates will be invited to formally apply for the PhD program. Funding Notes Funding will be provided for UK (home), EU, and international students with a tax free generous starting stipend of 20, 688GBP
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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in