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Research Centre of Cognitive Computing in Life Sciences

With Cognitive Computing, we offer new solutions for the life sciences that are based on the fundamental understanding of man and machine as a learning system.

About us

The Centre for Cognitive Computing for Life Sciences deals with the development and use of computational methods and models for the field of life sciences, whose properties are inspired by the learning and adaptive abilities as well as self-organisation principles of natural systems. The solutions developed support demanding human activities and decision-making processes or can be used for process automation. The following general aspects are central to our research and development: learning ability/adaptability of the systems, context-bound solutions (application context in the life sciences), systemic consideration of the application and the context.

The centre is divided into different research groups, each of which focuses on certain methods or application domains.

Our Research Groups

Bio-Inspired Methods and Neuromorphic Computing

The research group is methodologically specialised in the development and use of adaptive algorithms and nature-inspired methods (evolutionary algorithms, Physics methods). Furthermore, the specialisation also includes the use of neuromorphic technologies and the development of hybrid approaches that combine models of complex systems with data-driven solutions. The areas of application include all areas of the life sciences with a focus on the health domain as well as laboratories and production.

Autonomous Systems and Reinforcement Learning

The research group is methodologically specialized in reinforcement learning, unsupervised and semi-supervised learning as well as human-in-the-loop machine learning. The methodology therefore also covers topics such as online learning and agent-based systems.
Applications span all areas of the life sciences. A specific focus is on the development of machine learning solutions for applications in biotechnology.

Predictive Analytics

The research group's focus is on applied research in statistical modeling and machine learning for pattern discovery, as well as data mining, pattern recognition, and forecasting in life sciences. We have a proven track record in the areas of med-tech, personalized health and sports analytics.
The group's expertise lies in the fusion of heterogenous information sources and ensemble methods, in particular for time series and image/video analytics. In the context of Industry 4.0, the group conducts research in the area of predictive and prescriptive maintenance.

Digital Environment and Sustainability

The research group focuses on the modeling of natural systems and their interaction with humans. This also includes sustainability topics in a more general context such as in view of social and economical questions. We have a data science, machine learning and modeling approach to our problems. A special methodological focus is on modeling with discrete systems such as cellular automata and on deep learning methods.

Team Cognitive Computing in Life Sciences