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This PhD opportunity at Cranfield University invites ambitious candidates to explore the frontier of energy-efficient intelligent systems by embedding AI into low-power, long-life hardware platforms
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are looking for: A PhD (or have submitted your thesis before taking up the role) in Materials, Physics, Chemistry or a closely allied discipline Expertise in 4D-STEM data acquisition Expertise in cryogenic
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rhabdomyosarcoma), fragmented data from multiple small series has hampered global elementary understanding of metastasis biology and - therefore – targeted therapy development. It remains unresolved – and is
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Research theme: Materials 4.0 No. of positions: 1 Open to: UK applicants This 3.5-year project is funded by the School of Natural Sciences and is available for home students; the successful
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) in an appropriate discipline. Subject Area Computer Science & IT, Electrical & Electronic Keywords Artificial intelligence, floating-point arithmetic, numerical analysis, computer arithmetic, machine
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coding ability. Research Associate: Hold a PhD in Engineering, Mathematics or a closely related discipline, or equivalent research, industrial or commercial experience. *Candidates who have not yet been
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this testbed available to users for testing hardware and applications. NPL will lead on the testing and security evaluation of the testbed and collaborate widely on the technology development. The student’s
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quantification and data science. Potential investigation areas: • Enhancing Monte Carlo and Markov Chain Monte Carlo (MCMC) with reinforcement learning. • Developing adaptive tuning and continual learning
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One fully funded, full-time PhD position to work with Prof. Mahesh Marina in the Networked Systems Research Group at the School of Informatics, University of Edinburgh. The broad aim
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been identified. Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships