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processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs) into reliable and efficient decisions. The work spans
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Deadline: 30 September 2025 A fully funded 3.5 year PhD position is available to work on the project titled “Scalable benchmarking for digital quantum computers based on blind testing
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an implementation support package Project Supervisors: Dr Sam McLean Prof Beth Fylan Dr Justine Tomlinson Jaspreet Sohal Project Description: This is a fully funded training programme to conduct research
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural
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Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum and Stonewall University Champions Programme. Cranfield Doctoral Network Research
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, the University of Nottingham provides the perfect environment to carry out high-impact research that can genuinely shape the UK’s Net Zero future. This is your opportunity to create breakthrough materials
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the perfect environment to carry out high-impact research that can genuinely shape the UK’s Net Zero future. This is your opportunity to create breakthrough materials for clean energy technologies and develop
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consumables, equipment, and travel related to the project. Number Of Awards ReNU+ is a unique and ambitious programme that will train the next-generation of doctoral carbon champions who are renowned
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supervision across the Schools of Chemistry, Pharmacy & Pharmacology and of Environmental Sciences, gaining experience in both laboratory and computational approaches. You will benefit from two 3-months