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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.


Design and extension of artificial intelligence algorithms to map natural behavior






Complementary Application of Mathematical and Discrete-Event Models for Solving Complex Planning and Control Problems in the Offshore Construction Logistics







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




Knowledge Graph based Representation, Augmentation and Exploration of Scholarly Communication







Self-Learning Prediction Model for Completion Probability







The goal of the Simple-ML project is to significantly improve the usability of Machine Learning processes in order to make them more accessible for a braod user group