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including the Ground Operations Laboratory in the Digital Aviation Research and Technology Centre (DARTeC), and the suite of facilities in the new Cranfield Hydrogen Integration Incubator (CH2i
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sponsors to deliver the outputs and will have access to a bespoke training programme. Per- and polyfluoroalkyl substances (PFAS), also known as “Forever Chemicals”, are micropollutants of increasing concern
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Library/Information Science (or similar) and have substantive experience in an appropriate library environment preferably within the higher education sector, at least some of which should have been in a
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multitude of disciplines. As this technology becomes more prevalent, understanding the forensic fingerprints of these systems after a damage causing incident is critical to both investigators and engineers in
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would suit candidates with a sound background in engineering, computer science, or related disciplines. Funding This is a self funded opportunity. Find out more about fees here. Diversity and Inclusion
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are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here . Our Values and Commitments Our shared, stated values help to define who we are and underpin
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degree or equivalent in a related discipline. This project would suit candidates with a sound background in engineering, computer science, or related disciplines. Funding This is a self-funded PhD
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Organisation Cranfield University Faculty or Department Faculty of Engineering and Applied Sciences Based at Cranfield Campus, Cranfield, Bedfordshire Hours of work 37 hours per week, normally
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detection of chemical and microbial contaminants in rivers. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap