41 data "https:" "https:" "https:" "CMU Portugal Program FCT" 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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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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function of urban blue spaces influence perceptions. It will subsequently explore and evaluate the types of information and knowledge required to improve the understanding and appreciation of urban blue
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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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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for this research studentship, please complete the online application form. For further information contact us today: T: +44 (0)1234 758540 E: study@cranfield.ac.uk
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/position/type of hardware. Cranfield overview and Sponsor Information/Background: We have a long history in space systems, having undertaken space studies since the 1960s. Our current research has
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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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, intelligent monitoring systems and predictive technologies have become essential competitive advantages. This project sits at the intersection of data science, engineering, and design innovation, addressing
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those