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Imagine a world where sensors don’t just collect data, they think. With the exponential growth of the Internet of Things (IoT) and edge computing, the demand for ultra-low-power systems that can
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Applications are invited for an employed Doctoral Candidate to be funded by the Marie-Skłodowska-Curie Learning Network for Decentralized critical Infrastructure Asset Monitoring and coNDition
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monitoring. Conventional early-warning systems, such as optical/IR cameras, satellites, human observers, and dense sensor networks, either provide limited warning time, depend on clear visibility, or are too
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dynamic, optically unpredictable environments, dominated by waves, specular reflection, cloud-dependent illumination, and variable scattering. These factors significantly degrade sensor reliability, leading
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manufacturing and the UK’s move toward a net-zero economy. Motivation Lead a pioneering project tackling real-world challenges in laser processing. You’ll work with advanced experimental setups to design and test
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, access to finance, support organisations, and informal networks enable or constrain entrepreneurs’ digital trajectories. Working across selected emerging economy contexts, the research will combine
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that combines neural network based defect cause analysis with real-time adaptive scanning. It will significantly increase measurement efficiency, reduce labour costs, improve autonomous inspection accuracy, and
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network of UNESCO designations, especially in the focal areas of the Chair’s programme, as well as the UK. How to Apply Applicants should propose projects that reflect the objectives of the UNESCO Chair
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, and play an important part in accelerating the progression towards net zero in the UK and globally. In this PhD project, the student will lead on computational methods, including molecular dynamics (MD