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. Funding This is a self-funded PhD. Find out more about fees. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and
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flow visualisation and measurement techniques to study droplet impact under icing conditions to improve icing codes that aid in design and development of ice detection and mitigation 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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Project summary: A transition to net zero is desirable overall, but its costs and benefits are unevenly distributed across time, space, and between individuals, and policy to implement it must be
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abort (or not engage) if the bright white lines that fit a defined and rigid expectation are not clearly visible. These systems use algorithms, rather than AI machine learning, to detect road markings and
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access to state-of-the-art facilities and a network of professionals in the field. The expected impact of this research will be the development of valuable insights into how advanced technologies can be
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This studentship is open to both UK and International applications. This is a self funded PhD opportunity. Find out more about tuition fees. Cranfield Doctoral Network Research students at Cranfield benefit from
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to discover the fundamental mechanisms responsible for damage and deformation. About the host University and Through-life Engineering Services (TES) Centre Cranfield is an exclusively postgraduate university
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learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI