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Position Summary: Applications are invited for a PhD studentship, to be undertaken as part of the project “System Services in 100% Renewable Grids” at Imperial College London (Electrical and
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Engineering Department) and the National Gallery (Scientific Department). This studentship will be jointly supervised by Professor Pier Luigi Dragotti at Imperial College London (ICL) and Dr Catherine Higgitt
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for postdoctoral roles must hold a PhD or equivalent qualification in biology, epidemiology, computing, behavioural science or closely related disciplines, and should have a strong track record in data science
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materials science. The student will be jointly supervised by Dr Juhan Matthias Kahk and Prof. Marco Kirm (University of Tartu, Estonia) and Prof. Johannes Lischner (Imperial College, London). The studentship
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to present their findings at major international conferences and submit publications to refereed journals. Applicants should have a strong background in aerospace engineering or physics. The applicants should
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for enrolment for the PhD degree at Imperial College London. You will have a 1st class honours degree in mechanical engineering, mechatronics, electrical engineering, or a related subject, and an enquiring and
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Apply online: HERE Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application
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simulation codes. Applications are invited from candidates with (or who are expected to gain) a first-class honours degree or equivalent in a suitable field such as engineering, computer science, physics
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PhD Scholarship: Real-Time Brain Injury Prediction and Protection Framework for Intelligent Vehicles
supervision/support from Imperial College London for high-fidelity modelling of human head and advanced head injury criteria. The applicant: Applications are welcome from graduates with a biomechanics
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complexity and computational cost involved. This project will involve the developments of cutting-edge computational methods (such as surrogate models, data assimilation, optimization, and sensitivity analysis