Cutting-edge research in Machine Learning

The Fraunhofer CCIT Technology Hub Machine Learning develops new Informed Machine Learning solutions for intelligent systems. Our research activities focus on three main areas: Hybrid Learning, Simulation-based Learning, and Resource Aware Learning. With our basic and applied research, we improve the quality of products and optimize business and industrial processes. Our projects lay the foundations for innovative products, services, and forward-looking business models.

Do you have any questions concerning our research topics or are you interested in a collaboration? We are happy to hear from you.

Our main research areas

Hybrid Learning

Combining knowledge and data

Conventional Machine Learning methods yield suboptimal results if training data are biased, not representative, or otherwise lacking. Hybrid Learning algorithms address these issues by combining data- and knowledge driven techniques. Incorporating domain knowledge into structure or design of learning systems can provide them with common sense and leads to explainable solutions.

Simulation-based Learning

Integrated-physics based

In many application areas, data are still scarce but may be created by means of simulations. Simulation-based Learning integrates physics-based simulations, generative modeling, and resampling techniques to produce plausible representative training data as well as to improve simulation models.

Resource Aware Learning

Tailoring algorithms to individual devices

Machine Learning algorithms need to be tailored to the devices they are running on. Edge computing on IoT (Internet of Things) devices requires different solutions than learning on large clusters in computer centers and quantum machine learning deals with yet another kind of architectures.


Into a new dimension with Informed ML

Three questions for Prof. Dr. Stefan Wrobel, director of the Technology Hub Machine Learning

Prof. Dr. Stefan Wrobel
© Fraunhofer IAIS
Prof. Dr. Stefan Wrobel

What special role do the topics Machine Learning and Artificial Intelligence play in the cognitive internet?

Today, Artificial Intelligence and cognitive systems must be so efficient that they are no longer programmable but have to learn from data. With Machine Learning, we use the data and knowledge available for flexible and self-improving, intelligent systems.


What is the position of industry in this field, and how can Fraunhofer support enterprises?

The importance of Machine Learning is clearly recognized in the economy, but in many sectors on the one hand the appropriate data, and on the other hand the necessary specialists are lacking. We help enterprises assemble and create the right combinations of data, processes and business models. In our data science instruction courses at Fraunhofer, on site at the company or in interactive-online courses, we train the next generation of data scientists.


What are your long-term goals with the Technology Hub Machine Learning and its implications for the topic of »Cognitive Internet Technologies«?

With the Technology Hub Machine Learning, we want to establish »Informed Machine Learning« on a broad footing, by which we mean approaches that not only learn from data but can also utilize existing expert knowledge and models, as are often available in the economy, to improve performance. At the same time, the transparency and reliability of the results are assured, thereby bringing about the preconditions for trustworthy cognitive internet technologies.

Further information


Research in application

We apply our research findings in a large number of projects.


Get an impression of our research work

Discover all publications of our scientists at the Research Center Machine Learning.