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Field
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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which
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the performance of novel, renewable, wave energy harvesting approaches. Here the research ambition is to extend the state of art from small scale sensor networks (nW’s to mW’s), towards a vehicular scale (W’s to
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analytical and numerical skills, with a well-rounded academic background. •Demonstrated ability to develop precision mechanical devices and mechatronics •Ability to develop kinematic and/or dynamic analysis
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-rounded academic background ◾Demonstrated ability to develop precision mechatronics system and algorithms ◾Ability to develop kinematic and/or dynamic analysis of mechanical/robotic systems ◾Ability
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from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff
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will use advanced unsteady computational fluid dynamic methods for the analysis of coupled intake/fan configurations in crosswind and high-incidence conditions. The research will adopt these methods
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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on Data Science for Construction Productivity. The researcher's responsibilities will include
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Nottinghamshire NHS Foundation Trust. They will work as part of an established and diverse wider research team based at other recruitment sites in Sussex, London and Gateshead. You must have knowledge and
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AI design of ultrathin lenses for compact imaging devices The Faculty of Engineering at the University of Nottingham is seeking an enthusiastic, self-motivated student who enjoys working as part of
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AI design of ultrathin lenses for compact imaging devices The Faculty of Engineering at the University of Nottingham is seeking an enthusiastic, self-motivated student who enjoys working as part of