Ruhr University Bochum is part of the newly established ELLIS Unit NRW. As a research unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), the unit represents an important milestone in the development of a world-class ecosystem for AI and machine learning research in North Rhine-Westphalia.
The joint research focuses on open-source foundation models, trustworthy AI, and the transfer of research findings to real-world applications, such as in healthcare, sustainable agriculture, and robotics.
The RUB’s Faculty of Computer Science is represented by several researchers: Asja Fischer serves as the Unit’s Co-Director for Bochum and is a member of its executive board. In addition, Ivan Habernal, Nils Jansen, and Bilal Zafar are actively involved in the new research network. Together, they contribute their expertise in the fundamentals of machine learning, formal methods, and trustworthy AI, as well as at the intersections of AI and society and AI and security.
The ELLIS Unit NRW is being established under the leadership of Jürgen Gall (University of Bonn). Other participating institutions include RWTH Aachen University, Bielefeld University, the University of Cologne, TU Dortmund University, Forschungszentrum Jülich, and the University of Paderborn.
Building on the strong AI ecosystem in North Rhine-Westphalia, including the Lamarr Institute for Machine Learning and Artificial Intelligence, the RWTH Center for Artificial Intelligence, and Forschungszentrum Jülich, home to Europe’s first exascale supercomputer, JUPITER, and the AI Factory JAIF, the unit will bring together more than 40 principal investigators and over 300 doctoral candidates and postdocs to collaborate on new research initiatives, doctoral programs, summer schools, and collaborations with industry and public partners.
ELLIS is a pan-European AI network of excellence which aims to secure lasting international leadership of AI made in Europe by connecting top researchers in this field and by creating a multi-centric AI research laboratory.
General note: In case of using gender-assigning attributes we include all those who consider themselves in this gender regardless of their own biological sex.