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advanced planning and control algorithms, real-time and energy-efficient sensing, and distributed perception. Its research is applied across diverse sectors, including agriculture, manufacturing and remote
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the crossover between artificial intelligence and Archaeological Prospection. Archaeological prospection faces an unusual combination of challenges: data are sparse and unevenly distributed, observations
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an unusual combination of challenges: data are sparse and unevenly distributed, observations are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints
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of spectrum sensing and DoA algorithms on RFSoC platformsIntegration and validation within the DRONE-SENSE platform Responsibilities: Design and implement distributed passive RF sensing nodes using RFSoC
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of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously acquired knowledges
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thesis in the field of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously
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an increasingly complex, fragmented and distributed news media environment. This includes examining how people encounter, consume and engage with news across digital platform s, social media and new technologies
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communication and sensing performance. You will design and analyse new algorithms for beam forming, beam focusing, localization, and sensing using XL and distributed reconfigurable antenna arrays. The aim is to