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/learning based techniques in the areas of robotics, or autonomous systems, • interested in autonomous systems and signal processing, • Keen to work with equipment and embedded
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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performance simulation capabilities for gas turbine engines developed at Cranfield University as the starting point. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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capable of leveraging signals from terrestrial base stations, non-terrestrial networks such as LEO satellite, and complementary on-board sensors. Specifically, it will: To design reconfigurable airborne
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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of the key processes featuring PFAS strategy plans. It is a widely implemented process with well-known infrastructure and operation. However, while GAC regeneration frequencies for micropollutants such as
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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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realistic degradation from corrosion processes. The simulations will be integrated with mesoscale experimental to evaluate the constitutive response of smooth specimens degraded by corrosion. Given