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decisions related to turbine inspection and maintenance with a human expert [4, 5]. This can be based on a deep reinforcement learning framework, which interactively optimises key performance indicators in
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simulation skills while gaining deep expertise in electromagnetic propagation, sensor technology, and applied physics. Why Cranfield? Cranfield University is a recognised leader in defence, aerospace, and
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team, you will apply cutting-edge machine learning and deep learning techniques to dramatically reduce testing cycles. You will lead life cycle analyses, implement advanced health monitoring strategies
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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techniques, would be an advantage. The ideal candidate will have a deep interest in the algorithms that power graphics and a creative mindset, eager to think outside the box and develop novel solutions
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robots become increasingly integral in society, they must adapt to changing environments and cooperate with human partners effectively. Traditional AI systems, such as neural networks and deep learning
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the accurate prediction of reaction enthalpies and activation free energies for all relevant intermediates. In this project, a deep learning and generative design toolchain will be developed resulting in an ML
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and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and
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memorisation capabilities of deep learning models. Such vulnerabilities expose FL systems to various privacy attacks, making the study of privacy in distributed settings increasingly complex and vital