620 computational-physics "https:" "https:" "https:" "https:" "Simons Foundation" PhD positions in United Kingdom
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also be involved in a fixed number of teaching hours during your time with us. In year one, alongside your specific PhD research modules, you’ll complete a short teacher training programme by
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of Sport, Exercise, and Nutrition Education – kimberley.edwards@nottingham.ac.uk This project is not funded, and we are seeking a student who can self-fund the PhD. Programme description: The overall theme
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detection of chemical and microbial contaminants in rivers. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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, limited predictability and slow process optimisation. The PhD sits within an interdisciplinary research environment that combines laboratory experimentation with mechanistic and computational modelling
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been shown to increase the risk of depression, anxiety, reduced physical activity and poorer sleep (Alon et al., 2024; Orben et al., 2020). Despite growing concern, there is a need of a clear
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at the intersection of theory, computation, and high‑fidelity simulation, the successful candidate will contribute to the development of a novel ensemble-based framework for analysing driven perturbations in wall
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: School of Physics & Astronomy Studentship, in collaboration with Merck, providing the award of full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £21,805 per year
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Are you an outstanding and ambitious engineering or computer science graduate looking for the next challenge? Do you want to work at the frontier of artificial intelligence and robotics to enable
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio