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Field
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materials science and hydrogen technologies. The industrial sponsor, Airbus, is committed to net zero aviation by 2050 and is pioneering LH2 powered aircraft. This partnership provides a unique industrial
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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climate change-resilient infrastructure slopes. This PhD is co-funded and co-supervised by Network Rail. The aim of this project is to enhance the utility of InSAR (Interferometric Synthetic Aperture Radar
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. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network
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labs framework. This studentship will include cohort-based training and activities, enabling students to gain wider skills and develop valuable personal and professional networks. Project Description
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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reliable public transportation system. This project will give you access to unique field data and expert knowledge, and the opportunity to visit Network Rail and work with colleagues in Colombia. If you're
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Join our diverse and inclusive team to transform the future of aviation as part of the UK’s EPSRC Centre for Doctoral Training in Net Zero Aviation. Offering fully funded, multidisciplinary PhD