The Gütig lab uses analytical and numerical modeling techniques to identify the computational principles underlying spike based information processing and learning in central nervous systems and to understand how these principles are implemented by biological processes. Specifically, the lab focuses on the role of action potential timing in sensory…

The Medical Omics lab aims to improve healthcare and basic research by providing artificial intelligence-driven tools for the analysis of large biomedical datasets. For comprehensive insights in disease entities, we aim to include multiple layers of data, such as single-cell RNA sequencing, methylomics data and medical imaging. In close…

Common metabolic disorders such as obesity, insulin resistance and type 2 diabetes have both environmental and genetic causes. We now know that many sections of the genome influence the risk of weight gain or adverse fat distribution, in addition to the important roles played by lack of physical activity and excessive intake of high-calorie foods.…

The Haas group develops and applies novel multimodal single-cell and spatially-resolved technologies to study the complex etiology of hematological cancers and their interaction with the immune system.

Hematopoiesis describes the process of blood formation and is sustained by the activity of hematopoietic stem and progenitor cells throughout our lifetime. While our understanding of stem cell biology continues to grow, it still remains challenging to study the in vivo activity of individual stem cells in humans.

The main research area of the Bioinformatics and Translational Genetics group is the elucidation of single gene disorders (diseases caused by DNA mutations in a single gene). We are developing user-friendly software that can be used by non-IT specialists. This allows the physicians – who know most about the patients and their disorders – to analyse…

Brain simulation helps us understanding how our brain works – and developing personalized therapies for its diseases

Further advancing digital medicine requires innovative solutions for integrating healthcare and medical research data. From the perspective of medical informatics, there are important open questions. How can the required collection, integration and analysis of large and heterogeneous data sets be achieved in an efficient, effective and scalable…