23 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD positions at Cranfield University
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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
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of WAAMMat (https://waammat.com/ ), gaining valuable industry experience and exposure. The student is expected to acquire the following (including but not limited to) knowledge and skills from the research in
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Fibre reinforced composites have excellent in plane strength and stiffness and are being used in increasing quantities in aerospace, sports, automotive and wind turbine blade industries. However fibre reinforced composites are weak in their through thickness direction. This weakness can result...
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that conduct research with academic leaders across leading UK institutions. Engage in online and face-to-face activities, including cohort-building events and collaborative learning exercises • Funding: A
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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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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health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities
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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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biodiversity support, cooling, air quality regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits