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Field
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research network called NiteLiDAR. We want to use the results of this research, together with our partners, to create innovative demonstrators for the next generation of LiDARs. In the second step you will
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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Cranfield University is excited to invite applications for a PhD studentship focused on developing and validating innovative origami-paper eDNA sensors with community scientists for the rapid
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multimodal sensor systems—both as standalone sensor solutions and as networked sensor systems. Implementation is carried out as embedded systems in the laboratory as well as for deployment in field experiments
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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drone detection and localisation performance using radar systems. This can be achieved by improving the detection performance of individual sensors and by employing a cooperative network of sensors which
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methods for distributed state estimation in large and heterogeneous sensor networks, with particular emphasis on statistical model‑based approaches. As sensor systems scale up to vehicle fleets, drone
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systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups around the
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or complex interconnected networks. Importantly, both sensor and helper partners are required for resistance upon pathogen infection. In the ERC-funded project “CooPAIRation”, we aim to harness the genetic
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available in Digital Endocrinology in the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and