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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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mangroves peatlands, and streams, as nature based solutions. The lab integrates field-based experiments, high-frequency sensor networks, stable isotope approaches, and modelling to study water, carbon
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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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Aerial Vehicles (UAVs) sent to perform a mission, e.g. search and rescue operations, intelligent transportation systems and wireless sensor networks. The data can vary from image and video, GPS, GSM
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accepted all year round Details Future intelligent systems will involve the deployment of large-scale sensor networks in which hundreds or thousands of microsensors, unexpensive, small and lightweight
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include, but are not limited to: Responsibilities Design, build, and integrate robotic and automated systems, including end-effectors, manipulators, and sensor-actuator networks. Lead mechanical, electrical
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monitoring and transportation of people and goods. In these applications, several robots will share multi-sensor information eventually coming from infrastructure. The effectiveness of the proposed approaches
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communication technologies to ensure that our systems are highly secure and reliable in the context of comprehensive digitalisation and global networking: Our experts from the Competence Unit Cooperative Digital
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genetically linked pairs 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
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Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and clinical medicine to transform diagnosis and treatment of adrenal diseases. Digital medicine is