62 computer "https:" "https:" "https:" "https:" "UNIV" "UNIV" "UNIV" positions at Cranfield University
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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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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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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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. Diversity and Inclusion at Cranfield We are committed to fostering equity, diversity, and inclusion in our CDT program, and warmly encourage applications from students of all backgrounds, including those from
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, logistics and operations management, business analytics, Artificial Intelligence, computer science, or a related field would be particularly suitable. We would especially welcome candidates with an interest
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and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly
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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