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”. This exciting opportunity involves leading the development of advanced data-driven mathematical and computational models to suppress turbulence in pipe flows, contributing to pressing engineering efforts toward
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report to line manager(s) on a regular basis. Work with industry partners in the programme to define the basic component requirements as well as manufacturability metrics required for viability
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be used to demonstrate the ability to detect both gradual degradation and faults in the machine. Training & Skills You will benefit from a taught programme, giving you a broad understanding of the
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; computer vision; and machine learning. Ability to initiate, plan, organise, implement and deliver programmes of work to deadlines. Ability to work with people from different backgrounds and in team across
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Application/interview Good working knowledge of a relevant computer programming language, for example Matlab, Python or C/C++. Essential Application/interview Good working knowledge of software packages
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relevant Cluster Lead a 1-page expression of interest indicating the type of long-term fellowship they would like to apply to; how their research programme would enhance the activities in the School
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of the research programme as discussed with the fellowship supervisor. Contribute to research group planning. Coordinate and liaise with other members of the research group over work progress. Supervise research
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line with project objectives. Plan and conduct ecological monitoring fieldwork in three English cities, utilising novel instrumentation and tried-and-tested sampling methods (vegetation, birds, insects
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(PPPA) group at the University of Sheffield, in collaboration with other institutions and industrial partners, pursues a wide programme related to these muon applications. This PhD project is
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application statement. Essential criteria Must have a technical background with experience in the software development lifecycle. Previous experience as a software engineer or developer is crucial (assessed