Digital Landscape

To tackle major challenges emerging from digital technologies, the German government has established national competence centers with focus on machine learning, big data and IT security. The Federal Ministry of Research and Education (BMBF) will double the funding of the competence centers until 2022 to support their expansion. The map displays the leading institutions of this kind.



Competence Center Machine Learning Rhein Ruhr (ML2R)

ML2R aims at bringing machine learning (ML) technologies in Germany to a worldwide leading level. They establish cutting-edge research, support young scientists and strengthen technology transfer in companies through application-oriented research. The research foci are modular ML, ML with restricted resources, ML with complex knowledge as well as human-oriented ML. The Technical University of Dortmund, the Fraunhofer Institutes for Intelligent Analysis and Information Systems IAIS in Sankt Augustin and for Material Flow and Logistics IML in Dortmund and the University of Bonn are involved in the center. 

Contact: Prof. Dr. Katharina Morik, Technical University Dortmund
Tel.: +49 231 755 5101

National Research Center for Applied Cyber Security (ATHENE)

The National Research Center for Applied Cybersecurity ATHENE is a research center of the Fraunhofer-Gesellschaft with the participation of the Fraunhofer Institutes SIT and IGD as well as the universities Technische Universität Darmstadt and Darmstadt University of Applied Sciences. In a unique and innovative cooperation model of university and extra-university research, ATHENE conducts cutting-edge research for the benefit of business, society and government and strives for academic leadership. ATHENE is an agile research organization and therefore capable of responding quickly to new challenges and emerging threats. ATHENE is funded by the Federal Ministry of Education and Research (BMBF) and the Hessian Ministry of Science and Art (HMWK). 

Contact: Prof. Michael Waidner, Fraunhofer SIT
Tel.: +49 6151 869 250

Helmholtz Center for Information Security (CISPA)

CISPA was transformed in 2011 from a Center of IT-Security, Privacy and Accountability to a national competence center for IT security research. CISPA is a Helmholtz center that provides a comprehensive, holistic treatment of the pressing grand cybersecurity and privacy research challenges that our society faces in the age of digitalization. CISPA seeks to play a prominent international role on research, transfer, and innovation by combining cutting-edge, often disruptive foundational research with innovative application-oriented research, corresponding technology transfer, and societal outreach. It is deeply grounded in computer science and works interdisciplinarily with researchers in adjacent fields such as medicine, law, and the social sciences.

Contact: Prof. Dr. Dr. Michael Backes, Saarland University
Tel.: +49 (0)681 – 302 – 3249

Competence Center for Applied Security Technology (KASTEL)

KASTEL is one of three competence centers for cyber security in Germany, which were initiated by the BMBF in March 2011. Following the motto “Comprehensible security in the networked world”, KASTEL is meeting the challenges posed by the increasing interconnection of previously isolated systems. Of particular importance are the consequences of digitalization in the area of critical infrastructures, for example in the energy economy, in industrial production or networked mobility, but also in “intelligent” environments. The goal is to develop a widespread approach instead of isolated partial solutions. The focus will be on comprehensive security in specific application areas, such as power grids or intelligent factories.

Contact: Carmen Manietta, Karlsruhe Institute of Technology
Tel.: + 49 721 608-44213

Tübingen AI Center (TUEAI)

Tübingen AI Center aims to build learning systems that approach the versatility and robustness exhibited by natural intelligent systems. Machine learning is at the heart of a technological and societal revolution, although today’s learning systems do not generalize well to new situations, cannot learn from few examples, and do not infer causal relationships. Addressing these deficits and developing robust AI systems will help ensure technological leadership and thus help us deploy AI systems responsibly and to the benefits of society. The center is run by the University of Tübingen and the Max Planck Institute for Intelligent Systems as well as in collaboration with CyberValley.

Contact: Prof. Dr. Matthias Bethge, Eberhard Karls University of Tubingen
Tel.: +49 7071 29 89 017

Munich Center for Machine Learning (MCML)

The Munich Center for Machine Learning (MCML) is made up of leading researchers from the Ludwig-Maximilians-University Munich (LMU Munich) and the Technical University Munich (TU Munich). They are experts in the fields of data science, computer science and statistics. Pursuing the goal of strenghtening regional, national and international competence in the field of machine learning, MCML’s fundamental research is bundled in five competence areas of Spatial and Temporal Machine Learning (ML), Learning on Graphs and Networks, Representation Learning, Model Selection, Validation and Explainable ML, and Computational Models for Large-Scale ML. By linking fundamental research with applied research, the MCML aims at making a significant contribution to the development in the field of ML and thus expand and strengthen the competitiveness of Germany as a location for business and science.

Contact: Prof. Dr. Thomas Seidl, Ludwig Maximilian University of München
Tel.: +49 89 2180-9191

Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)

ScaDS Dresden/Leipzig was officially launched on 13 October 2014 as a national competence center for Big Data. In November 2019, ScaDS was expanded to a national competence center for artificial intelligence and received the new name ScaDS.AI.  The research project is driven by the partners Dresden University of Technology (TUD), the University of Leipzig (UL), the Max-Planck-Institute for Molecular Cell Biology and Genetics (MPI-CBG) and the Leibniz-Institute for Ecological and Regional Development. The competence center covers thematically important research challenges in data acquisition, data integration, knowledge extraction, visual analysis and utilization of large data sets for a broad spectrum of users. Furthermore, the center aims at closing the gap between the efficient use of mass data, novel AI methods of machine learning and the administration of knowledge.

Contact: Prof. Dr. Wolfgang E. Nagel, Technical University Dresden
Tel.: +49 (0)351 463-35450

Competence Center Machine Learning Berlin (BZML)

The Berlin Center for Machine Learning (BZML) aims at the systematic and sustainable expansion of interdisciplinary machine learning research, both in proven research constellations as well as in new, highly topical scientific objectives that have not yet been jointly researched. The efficient utilization of a priori knowledge in learning processes and the investigation of the effects of erroneous or incomplete data are crucial to this mission. In parallel, BZML experts further develop techniques for interpreting and explaining complex learning methods in order to arrive at more robust and, above all, more trustworthy models. Only then can statistical models be used to solve challenging scientific problems. In particular, the requirements and statistical peculiarities from the application areas biomedicine, digital humanities and communication are central to the BZML.

Contact: Prof. Dr. Klaus-Robert Müller, Technical University of Berlin
Tel.: +49 30 31478620

Berlin Big Data Center (BBDC)

The mission of the Berlin Big Data Center (BBDC) is to perform groundbreaking research and development, to train the “data scientists” of tomorrow and to create solutions that facilitate the deep analysis of massive amounts of heterogeneous data sets and streams at high velocity. In order to optimally prepare industry, science and the society in Germany and Europe for the global Big Data trend, highly coordinated activities in research, teaching, and technology transfer regarding the integration of data analysis methods and scalable data processing are required. To achieve this, BBDC is pursuing the following seven objectives: pooling expertise in scalable data management, conducting fundament research, developing an integrated, declarative and highly scalable open-source system, transferring technology and know-how, educating data scientists, empowering people to leverage “Smart Data” and enabling the general public to conduct sound data-driven decision-making.

Contact: Prof. Dr. Volker Markl, Technical University Berlin
Tel.: +49 30 314 23555