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on “ Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity ” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data
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backgrounds (e.g., uniform screens) and complex natural scenes Improving taxonomic classification, including the use of hierarchical classification schemes and anomaly‑detection methods Testing and validating
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, the developed models will be deployed in realistic scenarios, including turbulent flows over complex terrain, within built environments, and in wind farms. The project integrates fundamental applied mathematics
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models of complex physical systems starting from data, ranging from robotic systems to traffic and turbulent flows. We are implementing these methods in high-performance open-source libraries to make them