After a successful evaluation, the Competence Center Machine Learning Rhine-Ruhr (ML2R) has been transformed into the Lamarr Institute for Machine Learning and Artificial Intelligence and will receive long-term institutional funding from the German federal government and the state of North Rhine-Westphalia as of July 1, 2022. As an international center of excellence, the Lamarr Institute focuses on value-based research and development of powerful yet trustworthy and resource-efficient Artificial Intelligence.
Discover how the Lamarr Institute is building on the success of the Competence Center ML2R and expanding its research spectrum to decisively shape the future of European AI.
All the latest information about the Lamarr Institute can be found on our new web pages.
We look forward to your visit!
The Competence Center Machine Learning Rhine-Ruhr is one of six national nodes for bringing the development of Artificial Intelligence and Machine Learning in Germany to a worldwide leading level. To this end, we establish cutting-edge research, support young scientists and strengthen technology transfer in companies.
We connect pioneering research institutions in Germany: the Technical University of Dortmund, the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS in Sankt Augustin, the Fraunhofer Institute for Material Flow and Logistics IML in Dortmund and the University of Bonn. The close integration of basic and application-oriented research builds the ideal foundation for innovation. In this way, we actively shape Germany’s future and contribute to securing the digital sovereignty of our country. Because Machine Learning is the key to intelligent products, new business models and a head start in international competition.
Discover exciting articles on Machine Learning and Artificial Intelligence in our ML blog. Learn first-hand what ML2R researchers are currently working on, find out about possible applications in practice and expand your knowledge!
Trustworthy Machine Learning aims to increase trust in ML and AI applications using robust and verifiable techniques.
Hybrid Machine Learning integrates knowledge from different sources into learning systems to achieve reliable results even with small or uncertain datasets.
Resource-aware Machine Learning allows computations using ML to be performed reliably even on small devices, such as smartphones or directly in sensors.
Quantum computers can calculate a multitude of solution paths in parallel, allowing them to process information faster and handle more complex tasks.
