Computational Health Center

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 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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Our Research Areas

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Digital Genomics and Image Computing

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.

Artificial intelligence technology, modified

Health AI

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.

Sytems Biomedicine

Systems Biomedicine

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.

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News

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HMGU_Icon_Computat_Health

Featured Publication, Computational Health, ITG,

Tracking Metabolic Clues Years Before Type 2 Diabetes Develops

Researchers at Helmholtz Munich have found that the body begins signaling the risk of type 2 diabetes many years before the disease is diagnosed. In a long-term study, the team examined hundreds of blood metabolites – small molecules that reflect…

Osteoarthritis in knee

Awards & Grants, Computational Health, ICB, ITG, IML,

Helmholtz Munich Launches Pioneering Osteoarthritis Research Project With EU Partners

Helmholtz Munich is part of the EU research initiative PROBE, which aims to fundamentally transform the way osteoarthritis is diagnosed and treated. The project is funded through HORIZON EUROPE by the Innovative Health Initiative and has a total…

AI models in science

AI, Awards & Grants, Computational Health, ICB,

Helmholtz Munich to Advance AI Benchmarking With Three Newly Funded Projects

Helmholtz Munich has secured funding in the Helmholtz Association’s first-ever “UNLOCK” call for AI benchmarking. With three projects approved, the center plays a key role in developing high-quality, multimodal benchmarking datasets that will improve…

HMGU_Icon_Computat_Health

Featured Publication, Computational Health, Health AI,

TITAN: A Multimodal AI Model for Digital Pathology Slides

Scientists at Helmholtz Munich, together with Harvard Medical School and international collaborators, have developed TITAN, an artificial intelligence (AI) model capable of analyzing and describing digital pathology slides. By combining information…

Histology of human cells

AI, New Research Findings, Computational Health, ICB,

New Foundation Model Reveals How Cells Are Organized in Tissues

Researchers at Helmholtz Munich and the Technical University of Munich (TUM) have developed Nicheformer, the first large-scale foundation model that integrates single-cell analysis with spatial transcriptomics. Trained on more than 110 million cells,…

Prologue with Eric Topol

AI, Computational Health, ICB,

Prof. Eric Topol Visits Helmholtz Munich for Roundtable on “Virtual Human”

Helmholtz Munich had the pleasure to welcome Prof. Eric Topol, internationally recognized cardiologist, geneticist, and digital medicine expert, for a roundtable on the future of AI in healthcare. The event, serving as a prologue to the Bavarian…

Upcoming Computational Health Seminars

Christian Ledig

15.01.2026


Host: Carsten Marr
Title: TBD
Time: 16.00 (CET)
Location: Hybrid

Anna Bauer-Mehren

29.01.2026


Host: Annalisa Marsico
Title: TBD
Time: 11.00 (CET)
Location: Hybrid

Alex Tong

02.02.2026


Host: Antonio Scialdone
Title: TBD
Time: 11.30 (CET)
Location: Hybrid

Ben Lehner

12.02.2026


Host: Ele Zeggini
Title: TBD
Time: 16.00 (CET)
Location: Hybrid

Michael Ingrisch

26.02.2026


Host: Carsten Marr
Title: TBD
Time: 16.00 (CET)
Location: Hybrid

Our Principal Investigators

Explainable Machine Learning

Zeynep Akata

Deep Federated Learning in Healthcare

Shadi Albarqouni

Helmholtz Pioneer Campus

Nico Battich

Data Science and Intelligent Systems

Stefan Bauer

Translation Genetics

Na Cai

Systems Genetics and Machine Learning

Paolo Casale

Biostatistics

Christiane Fuchs

Computational Epigenomics

Maria Colomé-Tatché

Machine Learning and Data Analytics

Björn Eskofier

Efficient Learning and Probabilistic Inference for Science (ELPIS)

Vincent Fortuin

Computational Molecular Medicine

Julien Gagneur

Genetic and Epigenetic Gene Regulation

Matthias Heinig

Computation and Machine Learning

Dominik Jüstel

Reliable AI

Georgios Kaissis

Reliable Machine Learning

Niki Kilbertus

Immunogenomics

Sarah Kim-Hellmuth

Medical Genomics

Janine Knauer-Arloth

Immunogenomics

Daniel Kotlarz

Integrative Genomics

Malte Lücken

Institute of AI for Health

Carsten Marr

Computational RNA Biology

Annalisa Marsico

Computational Biomedicine

Michael Menden

Computational Statistics and Data Science for Biological Systems

Christian Müller

Neurogenetic Systems Analysis

Konrad Oexle

AI for microscopy and computational pathology

Tingying Peng

Helmholtz AI

Marie Piraud

Multiomics for Disease Diagnostics

Holger Prokisch

Machine Learning in Biomedical Imaging

Julia Schnabel

Translational Immunoinformatics

Benjamin Schubert

Human-Centered AI

Eric Schulz

Physics and data-based modelling of cellular decision making

Antonio Scialdone

Machine Learning and Data Science

Hannah Spitzer

Pioneer Campus

Lara Urban

Machine Learning

Fabian Theis

Metabolomics

Rui Wang-Sattler

Dynamical Inference

Steffen Schneider

Institute of AI for Health

Ewa Szczurek

Translational Genomics

Ele Zeggini

Accessible Biomedical AI Research

Sebastian Lobentanzer

Machine Learning for Biological Discovery

Michael Heinzinger

Machine Learning Model

International Conference Contributions

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

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Foundation Models

Health AI Fellows (HAIF) Program

The newly established 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. Despite remarkable progress, we are only beginning to tap into what computation and AI can achieve for human health.

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.

A Strategic Approach to Technological Readiness

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 is mentored by two CHC Principal Investigators, or optionally by one PI and one external expert from industry or a start-up. With this support structure, fellows have the freedom to develop and apply comprehensive computational strategies to tackle pressing challenges in health research. The program emphasizes not only scientific discovery but also strategic progress toward higher technological readiness levels.

Fellowship Package and Support

The HAIF Program provides fellows with the resources needed to establish their independent research trajectory. Each package includes:

  • A full-time postdoctoral position
  • Dedicated budget for travel and open access publications
  • Access to powerful computational infrastructure
  • Funding for a student assistant
  • Initial employment for two years, with the possibility of extension
  • Support in identifying third-party funding opportunities and preparing grant proposals through the CHC Grant Management team

Fellows also receive full access to Helmholtz Munich’s platforms and core facilities and benefit from mentoring by CHC faculty and external partners. They are encouraged to engage with the vibrant research ecosystem across Helmholtz Munich, local universities, and regional collaborators, fostering a highly interdisciplinary and innovative environment.
The call for applications will open in mid-December!

Apply here

Networks and Affiliations

Logo Technische Universität München

Technical University of Munich

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Logo Munich Center for Machine Learning

Munich Center for Machine Learning

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Single Cell Omics Germany


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Logo Ludwig-Maximilians-Universität München LMU

Ludwig-Maximilians-Universität München

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ELLIS Munich Unit Logo

Ellis Munich


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Logo CausalCellDynamics

Helmholtz International Lab: CausalCellDynamics

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Munich School for Data Science

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Contact

Frau Sacher, Anna, Dr._freigestellt
Dr. Anna Sacher

Head of Science Management & Administration, Institute of Computational Biology

Ingolstädter Landstraße 1, 85764 Neuherberg

Building / Room: 58a, 105

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Helmholtz AI

Democratising AI for a Data-Driven Future