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Field
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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
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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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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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PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded. Further information on the lab: https://www.pdn.cam.ac.uk/directory/ewa
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group of 50 PhD students, 7 PDRAs and 24 academic staff. We are a friendly a social group and interact frequently on academic and non-academic matters. Feasibility: You will use established lab and
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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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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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in spin-dependent processes in materials and devices for energy technology. More information can be found on the group website: taitgroup.web.ox.ac.uk . Please contact Dr Claudia Tait (claudia.tait