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that facilitate seamless integration between AI hardware components and embedded systems, ensuring efficient data flow and processing. Cranfield University offers a distinctive research environment renowned for its
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the solution of governing PDEs. - Train machine learning models to predict lifetime and failure based on loading and environmental histories. The PhD student will have access to world-class computing facilities
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colleagues in the Departments of Civil and Environmental Engineering and Physics. If you require any further details on the role please contact Dr. Sebastian Eastham s.eastham@imperial.ac.uk For your reference
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Engineering Department Location: Newport, Shropshire TF10 8NB Salary: £35,166 per annum Post Type: Full Time Contract Type: Fixed Term - Until 30 September 2028 Closing Date: 23.59 hours BST
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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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and Britain are world leaders and major exporters. This high technology global industry is worth more than £30 billion per annum. Current challenges are arising from the need to address environmental
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but not essential. A strong background in materials science and/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition
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, balancing efficiency and sustainability in AI deployment poses a significant challenge, calling for advances in model design and training to reduce environmental impact while maintaining high performance
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PhD Studentship: Carbon Nanotube (CNT) Winding Development for Electric Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers an exciting opportunity
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This project aims to bridge the gap between technological advancements and their integration into societal and environmental systems by shifting from a product-centric to a service-oriented approach