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should have a strong mathematical background, particularly in dynamical systems theory, and a keen interest in network science, and scientific computation. The student will gain invaluable experience
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supervisors spans five departments at University of Nottingham including Architecture and Built Environment, Electrical and Electronic Engineering, Mathematics, Physics and Social Sciences. The PhD programme
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leverage advanced bespoke continuum robotic systems to demonstrate the feasibility of applying the proposed coatings can be deployed in-situ. Ultimately, this work bridges the gap between the theory
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research into electric...
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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms
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benign reagents. Applicants should have, or expected to achieve, at least a 2:1 Honours degree (or equivalent if from other countries) in Chemistry or Mathematics or a related subject. A MChem/MSc-4-year
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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salary) for 3 years An additional £2,000 per annum for consumables and travel Requirements The candidate should have a 1st or high 2:1 degree in electrical/mechanical engineering, physics, mathematics
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, mathematics, or related disciplines. Skills in numerical tools and programming are desirable. Any experience in engineering design or manufacturing would be advantageous. Eligibility and Application Due
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aforementioned tasks with the following actions: Develop the principles and theories for governing the scalability principles for building innovative robotics end-effectors that can access geometrically complex