490 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"L2CM" PhD positions in United Kingdom
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: an innovative, interdisciplinary fully funded PhD programme that brings together science, engineering, and mathematics to tackle some of the most pressing challenges of our time. Awards for UK, EU, International
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environment in Europe. Applicants should have or expect to achieve at least a 2.1 honours degree in materials science/engineering, metallurgy, physics, chemistry, or a related subject. To apply, please contact
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Supervisors: Dr Angus Pettey and Dr Charles Heron One lesser considered challenge of the shift towards renewable energy is the need for reliable and robust energy storage facilities. A popular method of doing so is to create vast battery storage facilities which require incredible sums of...
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, including engineering, mathematics and the physical sciences. Students should be able to demonstrate aptitude for computer modelling and enthusiasm to address real-world problems of great policy significance
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developing neurotechnologies for treating brain disorders? In this PhD you will work with datasets of neuronal activity in animals and humans. You will apply computational approaches to describe spatial and
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project management, paper preparation and thesis writing, along with career support. The student will also have access to lecture material taught in the Faculty of Science and Engineering, and specific
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) programme. The post holder will work closely with Professor Ronita Bardhan and contribute to a high-impact, interdisciplinary research programme focused on advancing evidence-based urban design for population
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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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practice, and organisational culture. More broadly, it will contribute to debates on multi-agency relationships and the growing role of technology in criminal justice. A 3-month Research in Practice
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through