51 computing-"https:"-"https:"-"https:"-"UNIV"-"Univ" positions at Cranfield University
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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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. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme. Urban blue networks, including rivers, canals and wetlands, are dynamic systems that shape how cities
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scheme. Access to approximately 40 industrial, government & research partners from the wider aviation sector as part of the Net Zero CDT programme. Access to world class research and education facilities
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the Researchers Core Development programme alongside PhD students, together with access to the University Doctoral Core Research Methods Training (DCRMT) Programme courses, as well as a tailored programme of
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Confident Employer and proud members of the Stonewall Diversity Champions Programme. We are also committed to actively exploring flexible working options for each role. Find out more about our key
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are part of the programme. Entry requirements Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit students with an aerospace
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Centre focuses on applying cutting-edge approaches in synthetic biology, biotechnology, computational modelling and engineering science to deliver innovative solutions in bioengineering and bioremediation
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expertise in soil, plant and microbial systems across scales, with the Cranfield Environment Centre , which has expertise in environmental informatics and modelling and is the UK National Reference Centre for
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). The WIRe programme offers a bespoke training programme in technical and personal skills, and access to world-leading experimental facilities. The successful candidate will also have the opportunity
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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