Research Areas

Research in Bioinformatics, Medical Informatics and Systems Biology is rooted in the development of novel models, algorithms, and software tools dedicated to answering pertinent questions in the life sciences. Current and past research projects in Tübingen have dealt with developing solutions to challenging questions arising out of the diverse fields of phylogenetics, evolutionary of protein structures, structural bioinformatics, computational drug discovery, immuno-informatics, genomics, microbiome analysis, and gene expression analysis. Diverse and complex, these fields contain an immense opportunity for pushing the realm of knowledge and thereby solving contemporary problems in medicine, which require solutions to streamline the development of personalised cancer immune-therapies or close the gap in our understanding of microbiome-host interactions during an infection.

The Tübingen researchers in Bioinformatics, Medical Informatics and Systems Biology are also members in the following interdisciplinary centers:

• Institute for Bioinformatics and Medical Informatics (IBMI)
• Center for Quantitative Biology (QBiC)
• German Center for Infection Research (DZIF) 

Selected Current Research Projects

Collaborative Projects

• 2019–2026: Cluster of Excellence: Controlling Microbes to Fight Infections (CMFI)
• 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science
• 2019–2023: DFG-Joint Project: Omics Analysis and Bioinformatics, subproject Z03 of the TransRegio TRR 261 Cellular Mechanisms of Antibiotic Action and Production

The research groups in the field of machine learning cover a broad spectrum of topics, ranging from work on the theoretical foundations to scientific and industrial applications and the societal impact of machine learning. Current research topics include:

• Development of fair, robust, safe and secure machine learning systems
• Design of efficient learning algorithms in terms of hardware, computation, data and integration of prior knowledge
• Correct quantification of uncertainty in predictions
• Learning with multi-modal data, in particular with text, images, video, sound, etc.
• Learning with agents in dynamic environments
• Ethical and societal questions arising with the use of AI systems

We are collaborating with scientists in many different areas of science through the Cluster of Excellence “Machine Learning: New Perspectives for Science”, for example with researchers in computational neuroscience, medicine, biology, geoscience, and physics, but also in social science, law and philosophy.

We work closely together with other partners conducting research on AI in Tübingen:

• Max Planck Institute for Intelligent Systems
• Max Planck Institute for Biological Cybernetics
• ELLIS Institute Tübingen
• Hertie Institute for AI in Brain Health
• CZS Institute for Artificial Intelligence and Law

We are also part of larger initiatives in Tübingen, Germany and Europe:

• Tübingen AI Center
• Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA)
• ELIAS: European Lighthouse of AI for Sustainability
• Open Euro LLM 

The Tübingen AI Center organizes every year the Federal Competition for Artificial Intelligence (BWKI). 

Selected Current Research Projects

Collaborative Projects

• 2017–2029: Collaborative Research Center: Robust Vision (SFB 1233) (mit MPI-IS)
• 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science
• International Max Planck Research School for Intelligent Systems (IMPRS-IS) (et al. with MPI-IS)

Individual Projects

• 2025–2030: ERC Starting Grant to Claire Vernade: Continual and Sequential Learning for Artificial Intelligence (ConSequentIAL)
• 2024–2029: ERC Consolidator Grant to Philipp Hennig: Advanced Numerics for Uncertainty and Bayesian Inference in Science (ANUBIS)
• 2023–2028: ERC Consolidator Grant to Georg Martius: Model-based Reinforcement Learning for Versatile Robots in the Real World (REAL-RL)
• 2023–2028: ERC Consolidator Grant to Jakob Macke: DeepCoMechTome
• 2022–2028: Project founded by Carl Zeiss Foundation: Certification and Foundations of Safe Machine Learning Systems in Healthcare
• 2020–2025: ERC Starting Grant to Andreas Geiger: Learning Generative 3D Scene Models for Training and Validating Intelligent Systems (LEGO-3D)

The Software and Systems Engineering group is actively engaged in research that deals with the core practical and technical aspects of Computer Science from first principles to practical approaches. Applied projects are focused mainly on the development of large-scale software architectures, the analyses and transformation of structured data, algorithms for automatic evidence generation and optimization as well as the development of solutions for complex web-based distributed systems. Technical approaches are more focused on the analyses and optimization of complex embedded systems, and communication networks and computational architectures, all of which rely heavily on the application of machine learning algorithms.

Practical Computer Science

• Declarative Computer Languages for the Analyses and Transformation of Structured Data
• New Paradigms for Data-Intensive Programming
• Efficient Construction of Large Software Systems
• Definition, Analyses, and Verification of Software Systems
• Algorithms for Calculation of Mathematically Conclusive logic

Technical Computer Science

• Design and Verification of Secure Embedded Systems
• Timing - and Power-Analyses of Embedded Systems
• Architecture Design: From the Systems Level to Tapeout
• Neural Interfaces and Brain Signal Decoding
• Design, Optimization, and Application of Communication Networks
• Software-Defined Networking, 5G and the Internet of Things

Selected Current Research Projects

Collaborative Projects

• 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science

Theoretical computer sciences performs research on the very foundations of the field of computer science. It tries to answer fundamental questions such as:

• Which functions can be calculated by a computer in principle, and which ones cannot be computed?
• Which problems can be solved by efficient algorithms?
• How can we compare different algorithms, and in which sense are some algorithms "better" than others?
• Can computers "learn" to perform certain tasks, and if yes, which ones and how?

Moreover, the field of theoretical computer science develops formal frameworks that can be used by other branches of computer science to describe and analyze complex systems.

Selected Current Research Projects

Collaborative Projects

• 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science

The Department of Computer Science in Tübingen is home to a unique interdisciplinary research group which has a strong focus on visual cognition, multi-sensor and sensor-motors processing and their interactions with the goal of making connections to the abstract underpinnings of human cognitive mechanisms and neural encoding processes. In order to achieve these research goals, which drive the understanding of psychophysics and cognitive neurosciences, sophisticated techniques in advanced image analysis, robotics, intelligent software systems, and computer graphics are employed.

• Photo realistic 3D-Acquisition
• Mobile Robots
• Self-Learning Avatars in Virtual Reality Environments
• Hand Eye Tracking
• Computer Models of Human Vision
• Generative, Recurrent, and Self-organized Artificial Neural Networks

The department is also involved in the interfaculty Cognitive Science Center at the University of Tübingen. The CSC pursues the goal, together with the humanities and natural sciences, of gaining a deeper understanding of cognition. For cognition generates behavior, language, and thereby our culture. It is indispensably grounded in physics, biology, and neurobiology and can be understood through modeling using machine learning, mathematics, and statistics.

Selected Current Research Projects

Collaborative Projects

• 2017–2029: Collaborative Research Center: Robust Vision (SFB 1233) (mit MPI-IS)
• 2019–2026: Cluster of Excellence: Machine Learning: New Perspectives for Science

Individual Projects

• 2025–2029: ERC Starting Grant to Charley Wu: Compositional Compression in Cognition and Culture (C4)
• 2020–2025: ERC Starting Grant to Andreas Geiger: Learning Generative 3D Scene Models for Training and Validating Intelligent Systems (LEGO-3D)