We design the Machine Learning of the future, which is driven by both data and knowledge.

Machine Learning

Machine Learning (ML) is a key technology in relation to cognitive systems and Artificial Intelligence (AI). Thanks to progressively cheaper and more powerful sensors and processors ML techniques are increasingly driving digitalization and are determining factors in maintaining a competitive edge in many sectors. There is hardly any sector that isn’t currently undergoing a substantial transformation because of ML- and AI-based technology: from manufacturing and logistics through to medicine.

We have been at the forefront of applied ML research for many years. We shape the future of Machine Learning: modular Machine Learning, driven by data and knowledge. Our researchers are developing people-centered, comprehensible and reliable solutions.

We are also working on resource efficient applications that can be run on the most diverse range of hardware platforms. Our processes are tailored to specific application scenarios, from distributed Learning (Edge Computing) to quantum Machine Learning. Our learning algorithms can now be used for applications that have previously not been thought possible for commerce or society.

We are reporting on this technology, including its potentials and limitations in studies and scientific publications. We are actively involved in central research projects in Germany and the rest of Europe and keep a keen eye on the social debate over the impact intelligent machines might have on our lives.

Go to our publications

Research priorities

Big Data Analytics

Machine Learning involving extremely large volumes of data which are processed in real time on scalable platforms and infrastructures

Decentralized Machine Learning (Edge ML)

Learning on user devices or in the cloud without transmitting sensitive data in order to shorten response times and increase the efficient usage of distributed data

Informed Machine Learning

A combination of statistical learning and knowledge-driven approaches such as semantic methods based on knowledge graphs or physical system simulations

Text Analytics

The process used to analyze different kinds of texts in order to extract and summarize information or recognizing moods

Quantum Learning

The conversion of learning algorithms onto quantum computers for the purpose of solving computation-intensive problems that current, conventional computers are unable to solve

Visual and interactive Analytics

Processes aimed at performing explorative, interactive data analyses based on the visual abilities of people; main focuses are mobility prognoses and interactive model making


Center of Excellence Machine Learning Rhein-Ruhr ML2R

The Center of Excellence ML2R is one of four Centers of Excellence in Germany driving the development of Artificial Intelligence and Machine Learning on an international level. We conduct cutting-edge research, nurture a new generation of scientists and boost the integration of technology in companies. We develop modular Machine Learning applications and research ML and human-centric learning using limited resources and complex knowledge.

Research Center Machine Learning

The Fraunhofer Research Center consolidates the skills of three Fraunhofer institutes to further develop the idea of “Informed Machine Learning”. This method of research encompasses reliable ML technology and produces transparent and easily understood results. Informed ML opens up new application possibilities and makes it possible to use learning processes with only a limited amount of data and resources. The Research Center is part of the Fraunhofer Cluster of Excellence Cognitive Internet Technologies CCIT and its 13 institutes researching secure data rooms and cognitive skills for an industrial Internet.

"Machine Learning" Study

This study commissioned by the Fraunhofer-Gesellschaft defines the main Machine Learning terms, gives an overview of current challenges and future developments and details Germany’s position on the application of Machine Learning. With a focus on Germany, it also provides a general overview of the main players, application errors and the socio-economic parameters of Machine Learning.

The study is written in german language.