46 assistant-professor-computer-science-and-data-"Multiple" PhD positions at Cranfield University
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Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum and Stonewall University Champions Programme
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degree or equivalent in a related discipline. This project would suit individuals with academic or industrial experience in electronics, electrical engineering, systems engineering, or AI/data analytics
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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significantly reduce the amount of vibration data to be stored on edge devices or sent to the clouds. Hence, this project's results will have a high impact on reducing the hardware installation and operation
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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle
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members of the Business Disability Forum and Stonewall University Champions Programme. Additional information All CDT researchers are required to attend and successfully complete the taught training modules
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Applicants should have a first or second class UK honours degree or equivalent in in Design, Engineering, Computer Science/IT or a related subject. Experience in system design, and/or manufacturing is
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programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities. How to apply For further information please contact: Name: Dr Jelena
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research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects within WAMC. The student will become part of a diverse and dynamic
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised