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biodiversity, protection of ecosystem services, and climate change. Advisory services within these areas are offered to ministries and other authorities. Currently, about 150 staff and PhD students are working
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machine learning techniques to develop local graph representation models, which will be aggregated globally to enhance their predictive power and translational relevance, all while maintaining strict data
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Do you want to shape the future of digital nature monitoring? Do you have experience with LiDAR, AI, and ecology — and want to contribute to remote sensing research for biodiversity and ecosystems
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Do you want to shape the future of digital nature monitoring? Do you have experience with LiDAR, AI, and ecology—and want to contribute to remote sensing research for biodiversity and ecosystems
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data Design algorithms for correlating low-level events into process-level attack models Contribute to joint framework development with TU/e on continual learning Collaborate with industry partners
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. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop
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), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual