33 assistant-professor-computer-science-"https:"-"https:" PhD positions at Cranfield University
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the £65 million Digital Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations
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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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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle
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attracted intense interest from both academia and industry recently for its attempt to extract more useful information and knowledge from Big Data. Big Data analytics will help to develop more advance
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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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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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the subject area and continues to expand its research horizons. It plays a pivotal role in the £65 million Digital Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification
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strengths and interests (e.g. geospatial data science or socio-environmental modelling). Funding Sponsored by the Leverhulme Trust and Cranfield University, this Connected Waters Leverhulme Doctoral programme
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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