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Intelligent systems are characterized by learning processes that allow the acquisition of new skills and deal with a large amount of heterogeneous, uncertain and probabilistic data. Due to the complexity of the real-world situations represented by them, the automatic abstraction of information from data, the formation of appropriate representative models, and the semantic processing of existing information is essential to build intelligent systems for digital transformation, such as mobility To enable industry, medicine and education.


Internationales Zukunftslabor Künstliche Intelligenz

International Future Lab for Artificial Intelligence




Managed Forgetting

Today's flood of information makes it more and more difficult to concentrate on the really relevant and important things.





From perception to expectation to realization: comprehensive intermedia analysis on misinformation when migrating into the EU and potentially resulting threats.





Intelligent synchronization and semantic enrichment of position and video data for the analysis of sports gamesa




Design and extension of artificial intelligence algorithms to map natural behavior





Paving the Way towards Personalized Prevention and Care of Severe Norovirus Gastroenteritis








 A mapping of the origin and success of cooperation relationships in regional research networks and innovation clusters











ROXANNE - Real time netwOrk, teXt, and speaker ANalytics for combating orgaNized crimE







RuBICon (-OStnt)

Joint projects: RuBICon - Rule-Based Initialisation of Converter Dominated Grids

sub-projects: Methods for network reconstruction by decentralized generation plants