79 phd-computer-science-"IMPRS-ML"-"IMPRS-ML"-"IMPRS-ML" positions at Cranfield University in United Kingdom
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Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum and Stonewall University Champions Programme
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This self-funded PhD opportunity is open to both UK and international students with a strong background or willingness to develop expertise in offshore engineering, human factors, digital twin
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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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Applicants should have a first or second class UK honours degree or equivalent in in Design, Engineering, Computer Science/IT or a related subject. Experience in system design, and/or manufacturing is
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experience align with the research and training programme, followed by questions from the interview panel. At a glance Application deadline10 Sep 2025 Award type(s)PhD Start date20 Oct 2025 Duration of award4
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, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
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This PhD project offers a unique opportunity to delve into the complexities of free-market systems and sustainability through a novel ensemble prediction model. With a focus on addressing
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This is an exciting opportunity for candidates who would like to complete an MSc by Research in any topic that fits within the broad remit of design engineering for which the expert supervisor/s
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The MSc by Research in Forensic Science provides you with the opportunity to develop knowledge, understanding and expertise in your chosen field of research, in much greater depth than a taught
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised