Nine million for new research groups: revolution in machine learning!

Nine million for new research groups: revolution in machine learning!

On April 1, 2025, the German Research Foundation (DFG) announced the start of five new research groups. One of the groups will work at the Institute for Control Engineering under the leadership of Leibniz University Hannover (LUH). This research group is entitled: “Active learning for dynamic systems and control – data informativeness, uncertainties and guarantees” and focuses on machine learning in dynamic systems. In total, the DFG is providing around 19 million euros for these newly founded groups, which will work over a period of four years, with the possibility of a second funding period of four years.

The new approaches pursued in the research group are particularly relevant for future-oriented technologies and applications. The challenges in the field of machine learning are complex and include, among other things, the safety guarantees required in both autonomous driving and human-machine interaction. Traditional processes often do not offer such guarantees, making the development of innovative strategies essential.

Cooperation partners and possible applications

The research group cooperates with recognized institutions such as the University of Freiburg and the technical universities in Hamburg, Ilmenau and Munich. The research results could have far-reaching applications, particularly in robotics and energy technology. Machine learning has already been established in many areas, including medical diagnosis and autonomous driving, where targeted data analysis and learning systems are crucial.

Machine learning itself is a key technology for cognitive systems and is central to global economic development. Neural networks play a crucial role here. These are inspired by the nerve cell connections in the human brain and consist of multiple layers of data nodes connected to each other via weighted connections. Using “deep learning” methods, neural networks benefit from increasingly deeper layers that support them in solving complex problems. These networks are trained by repeatedly presenting data, which enables a more precise classification of how iks.fraunhofer.de explained.

Social acceptance and challenges

Social acceptance of these technologies is crucial for their spread. The debate about machine learning and artificial intelligence is often characterized by half-knowledge, which is why a fact-based discussion of the topics is necessary. A comprehensive study carried out as part of a BMBF-funded project provides an overview of current challenges and research questions in the field of ML. The project was initiated by Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS and the Fraunhofer Center for International Management and Knowledge Economy IMW. It provides valuable insights into the fields of application and socio-economic framework conditions that are important for research in Germany.

Given the rapidly advancing technologies in machine learning and the multitude of possible application scenarios, it remains to be seen how the new research groups at LUH will contribute to the further development of these technologies, especially with regard to security guarantees and innovative learning methods.