73 professor-computer-science-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Cranfield University
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Are you passionate about developing novel research and keen to shape the future of energy transfer technologies in areas such as, forensic science and Uncrewed Aerial Systems (UAS). We
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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infrastructure. This project sits at the interface of environmental engineering, water quality management, and sustainable infrastructure. This PhD project will explore how ICWs can be strengthened through design
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fees. 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
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designing research approach and drawing on a wide range of social science methods. Key commercial sectors include (but are not limited to) data centres and high-tech industries, as well as food and beverage
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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engineering, digital technologies, and systems thinking. The university’s strong reputation for applied research and its focus on technological innovation ensure that this project will be well-supported, with
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are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here . The role of the Finance Professional Service Unit is to ensure that all financial data is
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in an increasingly volatile landscape and this PhD programme offers students the opportunity to study the strategic, organisational, and policy challenges facing defence and security institutions. It
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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