38 assistant-professor-computer-science-data-"https:"-"https:" PhD positions at Cranfield University
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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
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degree or equivalent in a related discipline. This project would suit individuals with academic or industrial experience in electronics, electrical engineering, systems engineering, or AI/data analytics
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more research is required to establish the inter-dependencies between these properties with the help of high resolution images. In this research, different sets of composites material will be dynamically
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Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities. How to apply For further information please
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range of future careers. Should there be interest, there is also the possibility of developing teaching and supervision skills on our MSc Astronautics and Space Engineering programme. At a glance
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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years EligibilityUK, EU, Rest of world Reference numberSATM450 About the host University and Through-life Engineering Services (TES) Centre Cranfield is an exclusively postgraduate university that is a
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present at international conferences. An industrial placement within Thames Water’s Engineering Innovation team will provide commercial insight and help you build a CV that stands out in both academic and
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This is a self-funded PhD position to work with Dr Adnan Syed in the Surface Engineering and Precision Centre. The PhD project will focus studying high temperature corrosion mechanisms in details
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, environmental science, urban sustainability, geospatial analysis, or quantitative modelling. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods