Computational Health

We develop novel computational tools powered by AI to accelerate discovery and translation. We apply cutting-edge computational methods to promote personalised health. Collaboratively, we develop predictive algorithms as well as mechanistic models to analyse molecular, imaging, and clinical data of human health and disease. We thus help to create innovative diagnostics and novel treatments for environmentally triggered diseases.

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We develop robust methods for analyzing big data to address key biomedical challenges and consolidate analytical approaches using innovative digital methods. In addition, we develop novel statistical methods for trans- ethnic meta-analysis, testing for pleiotropy, rare variant burden, testing, and polygenic risk score construction.

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We develop and translate AI technologies for biomedical problems by constructing deep-learning methods and combining them with more mechanistic modeling approaches. In addition, we steer the computational aspects of developing single-cell atlases in healthy and disease state to build AI-driven analytics platforms for multimodal data, in particular from genomics and diverse imaging modalities.

We develop novel computational methods for multiomic data integration of epigenomic, transcriptomic, proteomic and genetic data and advanced phenotypic/in vivo observations. In addition, we design novel approaches for efficient data combination across omics levels, maximizing the information yield across the multidimensional space of datar from genomics and diverse imaging modalities.

A Journey into the Secrets of Human Cells

What the Human Cell Atlas (HCA) will tell us about ourselves and how scientists at Helmholtz Munich are facilitating this revolutionizing global endeavor.

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Using AI in Medicine to Combat Diseases

Artificial Intelligence (AI) is becoming increasingly crucial in medical research, and Helmholtz Munich is one of the world's leading institutions in this field. It can detect tumors as effectively as severe eye diseases.

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Big Data at Your Bedside

Analysis of genes can help in detecting diseases at an early stage as well as in determining new drug targets for therapy. Thanks to extremely powerful data processing, the field holds great promise for advancement. Helmholtz Munich is particularly at the cutting edge in the use of these new methods.

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In the future, blood samples from patients can be evaluated not only by specialists, but also by artificial intelligence. A team from Helmholtz Munich is laying the foundations for this.

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How AI is Revolutionizing Medicine

Computer systems can already independently analyze health data, learn from it and even derive therapy recommendations. Which opportunities this means for patients and what research needs to apply artificial intelligence in practice.

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Daniel Kotlarz, group leader at the Institute of Translational Genomics at Helmholtz Munich and Heisenberg Professor of Precision Medicine of Pediatric Inflammatory Bowel Diseases at LMU University Hospital Munich receives around 600,000 Euros…

FEBS|EMBO Women in Science Prize for Eleftheria Zeggini

Prof. Eleftheria Zeggini has been awarded the FEBS | EMBO Women in Science Award 2027 for her pioneering contributions to human genomics and the translation of large-scale genetic discoveries into biological and clinical insights, alongside her…

Cell membranes and the proteins within them control many vital processes and play a key role in health and disease. But studying them in 3D images of cells has so far meant slow, manual work. A team from Helmholtz Munich, the Technical University of…

Dr. Reid Alderson from the Institute of Structural Biology and the Institute of Computational Biology at Helmholtz Munich has received a Starting Grant from the European Research Council (ERC). Through the Protein-DANCE project, he will develop a new…

Dr. Marcel Binz from the Institute for Human-Centered AI at Helmholtz Munich has received a Starting Grant from the European Research Council (ERC). Through the MIDAS project, he will develop large-scale computational models designed to simulate,…

A new interdisciplinary team at Helmholtz Munich aims to bridge one of healthcare AI's longstanding challenges: translating promising algorithms into tools that perform reliably in clinical practice. The M1 Clinical AI Consultants, based at the…

22.09.2026

Host: Iva Pritisanac
Title: TBD
Time: TBD
Location: Hybrid

22.10.2026

Host: Iva Pritisanac
Title: TBD
Time: 16.00 (CEST)
Location: Hybrid

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Follow the link to find the latest contributions from Computational Health Center researchers at international AI conferences:

Postdoctoral Health AI Fellows Program

The newly established Postdoctoral Health AI Fellows (HAIF) Program is dedicated to fostering independent, high-impact research in computational biomedicine and health-related AI. Our mission is to empower early-career postdoctoral scientists from computational, mathematical, engineering, and physical science disciplines by granting them early scientific independence to boldly address key challenges in computational biomedicine.

Recent advances in computation have transformed our understanding of molecular processes and their effects on health and disease. Breakthroughs in modeling, AI, and large-scale data analysis have reshaped biomedicine, enabling researchers to extract meaningful insights from increasingly complex biological datasets. As high-throughput technologies generate vast volumes of data, new challenges emerge in scale, management, analysis, and interpretation. Yet, these same developments open unprecedented opportunities to uncover complex biological mechanisms, predict system behavior, and understand pathophysiological processes.

Unlike traditional postdoctoral positions, HAIF fellows join the Computational Health Center (CHC) at Helmholtz Munich as independent early-career researchers with greater autonomy. Each fellow has access to mentoring by two senior researchers, either two CHC Principal Investigators or one Principal Investigator and one external expert from a university, industry, or a start-up. With this support structure, fellows have the freedom to develop and apply comprehensive computational strategies to tackle challenges in health research.

By leveraging experimental, computational, and statistical approaches, the HAIF Program empowers fellows to explore new scientific frontiers that integrate data-driven and experimental methodologies. Working closely with experimental partners in Munich and beyond, fellows bring fresh perspectives to significant biological and biomedical challenges—stimulating innovation and driving the development of transformative solutions.

The call for applications will open in mid-December!

Kadri, S. ; Mattner, L. ; Zeng, Z. ; Prakki, S.R.S. ; Verma, A.K. ; Cetin, U. ; Mayr, C.H. ; Ansari, M. ; Wei, X. ; Asgharpour, S. ; Wasik, A. ; Kneidinger, N. ; Stoleriu, M. ; Behr, J. ; Polleux, J. ; Yildirim, A.Ö. ; Sadeleer, L.J.D. ; Wuyts, W. ; Burgstaller, G. ; Mann, M. ; Mück-Häusl, M. ; Schiller, H.B.

Mechanosensitive phosphorylation of NFATC4 at S213/S217 drives fibroblast-to-myofibroblast transition and fibrosis.

Berger, C. ; Kempa, S. ; Jung, E.M.

Imaging Techniques for Adipose Tissue Analysis.

Semmler, L. ; Büchner, B. ; Kornblum, C. ; Deschauer, M. ; Wortmann, S. ; Morgia, C.L. ; Servidei, S. ; Freisinger, P. ; Mensch, A. ; Schuelke, M. ; Prokisch, H. ; Mancuso, M. ; Lamperti, C. ; Klopstock, T. ; Bertini, E. ; Bischoff, A.T. ; Boy, N. ; Bruno, C. ; Carelli, V. ; Cecchi, G. ; Claeys, K. ; Distelmaier, F. ; Filosto, M. ; Garone, C. ; Hempel, M. ; Karall, D. ; Kmop, C. ; Kotzaeridou, R. ; Lopriore, P. ; Mongini, T. ; Montano, V. ; Musumeci, O. ; Nicoletta, V. ; Primiano, G. ; Procopio, E. ; Rinaldi, R. ; Ruggiero, L. ; Santer, R. ; Sasse, H. ; Schäfer, J. ; Schlein, C. ; Schöls, L. ; Strube, D. ; Thaele, A. ; Valentino, M.L. ; Kleist, J.v. ; Zeng, L.

Mitochondrial diabetes mellitus: Real world insights from the GENOMIT registry–a multinational, longitudinal cohort study.

Primio, C.D. ; Hafeez, K.S. ; Casalone, E. ; Filomena, R. ; Rosselli, M. ; Russo, A. ; Allione, A. ; Guarrera, S. ; Riccardi, B. ; Balfanz, P. ; Jacobs, B. ; Devaux, Y. ; Bermejo, J. ; Heymans, S.R.B. ; Gaborit, B. ; Bergerot, C. ; Sohler, F. ; Liechti, R. ; Martin, O. ; Lascano-Maillard, J. ; Ibberson, M. ; Audureau, E. ; Wang-Sattler, R. ; Lang, C.C. ; Sam, F. ; Derumeaux, G. ; Matullo, G.

Unmasking subclinical cardiac dysfunction in type 2 diabetes through genetic and epigenetic profiling.

Chalabi, M. ; Coorens, T. ; Li, L. ; Reading, J.L. ; Shen, S. ; Szczurek, E.

The next 25 years of cancer research: Emerging perspectives and priorities.

Miljanić, K. ; Žigman, T. ; Tomac, V. ; Pušeljić, S. ; Zrno, N. ; Fumić, K. ; Ozretić, D. ; Mayr, J.A. ; Prokisch, H. ; Barić, I. ; Petković Ramadža, D.

Clinical, neuroimaging, genetic, and outcome characteristics of Leigh syndrome: Experience from a single tertiary center.

Madni, H.A. ; Umer, R.M. ; Marr, C. ; Foresti, G.L.

MOSAIC: Maximizing out-of-distribution sensitivity via aligned image classification.

Kupecz, K. ; Galla, Z. ; Losonczi, R. ; Volford, D. ; Siska, A. ; Sejben, A. ; Kohistani, M. ; Kis, M. ; Somogyi, R. ; Greschik, Z.A. ; Kriston, A. ; Kovács, F. ; Horvath, P. ; Földesi, I. ; Monostori, P. ; Cserni, G. ; Kahán, Z. ; Sárközy, M.

Potential role of tryptophan metabolites in the sex-based differences in doxorubicin-induced chronic cardiotoxicity in a rat model.

Foundation Models in Health Research

Foundation models are advanced artificial intelligence systems designed to process vast amounts of data and to complete a range of downstream tasks. Being trained on extensive datasets these models are now revolutionizing biomedical research, accelerating discovery and translation. At the Computational Health Center we and our partners leverage these cutting-edge models to decode the complexities of human biology, aiming to pave the way for innovative treatments and a deeper understanding of health and disease.

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