Data Science and Artificial Intelligence
Cross-Cutting Area at the Max Delbrück Center
About us
Sixteen Labs and four Technology Platforms collaborate across scales and data types to tackle complex biomedical challenges – advancing the frontiers of Biomedical AI.
Our AI and data science methods integrate data from genomics, imaging, and clinical records to uncover disease mechanisms, enhance diagnostics, and inform personalized therapies.
This work is driven by interdisciplinary teams of researchers from academia, industry and university hospitals to ensure that new methods and insights are robust and widely applicable.
Research
We apply machine learning and statistical methods to analyze biomedical data at the molecular, cellular, and population level. By combining diverse data types – such as genomics, imaging, and clinical records – we build models that reveal how diseases develop and progress.
Our goal is to make these models both biologically meaningful and clinically useful. Achieving this requires close collaboration between computational and experimental teams, supported by shared technology platforms and interdisciplinary expertise. The result: insights that drive new diagnostics, therapies and precision medicine.
- Omics and precision medicine
We develop machine learning models to infer gene regulatory networks and understand how regulatory information is encoded in the epigenome and non-coding genome.
Leveraging high-resolution single-cell multi-omics, we aim to interpret patient genomes in diverse contexts – including cancer cohorts, patient-derived organoids, and in-vitro systems that model disease-specific molecular phenotypes.
- Data integration and disease modelling
We integrate genomic, proteomic, and phenotypic data into mechanistic, multiscale models to uncover disease mechanisms across molecular, cellular, and organ levels.
A key strength lies in linking heterogeneous data types to biological function through mathematically grounded approaches.
- Biomedical image analysis and complex phenotyping
We develop algorithms for processing, visualizing, and analyzing large-scale imaging data – enabling integrative, data-driven classification to improve diagnostics and personalized therapies.
Our disease-modeling platforms range from in vivo and live imaging to whole-organism and single-molecule resolution.
- Epidemiology and health data integration
We integrate large-scale clinical and epidemiological datasets – including cohort studies and health records – to connect phenotypic variation with disease mechanisms.
By harmonizing data across patients and study centers, we enable bidirectional translation between healthy populations and disease cohorts, and facilitate the analysis of treatment responses.
- Data Science Platforms
Data Science Platforms at the Max Delbrück Center provide cutting-edge technologies, instrumentation, and methodologies – supporting both established workflows and emerging applications.
Our platforms help drive collaborative research and enable us to develop new computational and experimental approaches.
- Software
Our open-source and in-house software tools power data processing, analyses, and visualization across diverse biomedical applications.
Access our tools in the research software directory of Helmholtz
Collaborations
We collaborate across disciplines and institutions to advance data-driven biomedical research. Through joint projects, shared infrastructures, and strategic partnerships, we accelerate innovation and foster a vibrant, interdisciplinary data science community.
- BeLOVE
- Berlin Long term Observation of Cardiovascular Events
Jointly with Charité researchers within the Berlin Institute of Health (BIH), this project follows circa 10,000 subjects with primary cardiovascular disease or key precursor type 2 diabetes. BeLOVE allows for direct observation of disease comorbidities, study of mechanisms and differential risk factors and determinants of treatment efficacy.
- BIFOLD
- Berlin Institute for the Foundations of Learning and Data
BIFOLD aims to conduct research into the scientific foundations of Big Data and Machine Learning, to advance AI application development, and greatly increase the impact to society, the economy, and science.
- de.NBI
- German Network for Bioinformatics Infrastructure
The de.NBI is a national, academic and non-profit infrastructure supported by the Federal Ministry of Education and Research providing bioinformatics services to users in life sciences research and biomedicine in Germany and Europe.
The partners organize training events, courses and summer schools on tools, standards and compute services provided by de.NBI to assist researchers to more effectively exploit their data.
- Helmholtz AI
- Helmholtz Artificial Intelligence Cooperation Unit
Helmholtz AI is one of five platforms initiated by the Helmholtz Information and Data Science Incubator. Its main goal is to become a driver for applied artificial intelligence (AI) through the development and distribution of AI methods across all Helmholtz centres, effectively combining AI-based analytics with Helmholtz' unique research questions and datasets.
- HFMI
- Helmholtz Foundation Model Initiative
HFMI, launched in February 2024 by the Helmholtz Association, aims to harness the transformative power of Foundation Models to address significant societal challenges and push forward AI development both within Helmholtz and beyond, bringing innovation and novel solutions into a variety of scientific fields.
- HIDA
- Helmholtz Information and Data Science Academy
HIDA connects and serves as the roof to 6 data science research schools linked by a network of 14 national research centers and 17 top-tier universities across Germany. HIDA was developed by the Helmholtz Information and Data Science Incubator that was founded in 2016. The Incubator is a body of 38 expert scientists from each of the Helmholtz Centers and industry experts.
- HIP
- Helmholtz Imaging Platform
The Helmholtz Imaging Platform (HIP) brings scientists and engineers in the Helmholtz Association together to promote and develop imaging science and to foster synergies across imaging modalities and applications within the Helmholtz Association.
- LifeTime
LifeTime, a pan-European initiative involving 50+ research institutes in 18 countries. Its goal is to track the molecular make-up of human cells in time and space at single cell resolution in order to be able to predict onset and course of diseases. An entire work package is focused on “Data Science, Artificial Intelligence and Machine Learning”.
The initiative is jointly coordinated by Nikolaus Rajewsky from the Max Delbrück Center and Geneviève Almouzni from the Institut Curie.
- NaKO
- German National Cohort
The Max Delbrück Center hosts a study center for the NaKO, which tracks health trajectories on a population level over longer time scales.
- PCAWG
- Pan-Cancer Analysis of Whole Genomes
The PCAWG study is an international collaboration to identify common patterns of mutation in more than 2,800 cancer whole genomes from the International Cancer Genome Consortium. The Schwarz Lab is part of PCAWG Working Group 3 (Interaction of Genome and Transcriptome) and is responsible for conducting allele-specific expression analyses to understand the impact of somatic genetic variation on gene expression in these 2800 tumours.
- sparse2big
In the sparse2big consortium eight Helmholtz Centers work together on developing, evaluating and sharing methods for data imputation and integration, with the scope to achieve meaningful big data and in-depth insightful analyses. Potential use cases range from patient data in medicine to remote sensing in geography or sample noise in imaging.
Training
Doctoral training
We train the next generation of data scientists through active participation in interdisciplinary PhD programs – leading HEIBRiDS, iNAMES, and Regulatory Genome, and partnering in CompCancer and other research schools.
- CompCancer
CompCancer is a PhD programme (DFG funded research training group) that focusses on computational aspects of cancer research. The goal of CompCancer is to develop and apply computational methods on relevant questions of current cancer research and thereby train the next generation of computational oncologists.
- HEIBRiDS
- Helmholtz Einstein International Berlin Research School in Data Science
The Max Delbrück Center is one of the six Helmholtz Centers that have joined forces with the Einstein Center Digital Future to create a new PhD program in data science. Established in 2018, HEIBRiDS is an interdisciplinary school that trains young scientists in Data Science applications within a broad range of natural science domains, spanning from Earth & Environment, Astronomy, Space & Planetary Research to Geosciences, Materials & Energy and Molecular Medicine.
- iNAMES
- MDC-Weizmann Helmholtz International Research School (HIRS) for Imaging from the NAno to the MESo
The Max Delbrück Center, the Weizmann Institute of Science in Rehovot, the Humboldt-Universität zu Berlin and the Charité – Universitätsmedizin Berlin have joined forces to establish iNAMES. The mission of iNAMES is the training of outstanding young imaging and data scientists in a truly international research school.
- Regulatory Genome
In an alliance between Berlin institutions (led by Humboldt-Universität zu Berlin) and Duke University, the DFG-funded international research training group Dissecting and Reengineering the Regulatory Genome aims to teach the next generation of researchers a quantitative understanding of genome function and gene regulation within the context of biological systems.
MDC-funded PhD positions in Data Science
Our Data Science group leaders are active in the Max Delbrück Center Graduate Program, which hosts PhD recruitment rounds twice a year.
MSc Training
Max Delbrück Center faculty contribute to MSc programs offered by our partner universities.
- Master Program Data Science
The Master Program Data Science is offered by the Department of Mathematics and Computer Science of Freie Universität Berlin. It is aimed at students who wish to specialize in the processing and analysis of large amounts of data.
- Master Program in Bioinformatics
Employing adequate training in the various sub-disciplines, this program provides the required knowledge for students to be able to judge mathematical methods and models, to recognize relevant biological questions, and to correctly interpret the results of the models in a biological context.
- Master Program in Biophysics
The Master Program in Biophysics of Humboldt-Universität zu Berlin offers research-based teaching in the interdisciplinary field of experimental and theoretical biophysics.
Publications
News & Events
Events
HEIBRiDS Lecture Series
In the bi-weekly HEIBRiDS Lecture Series, data science experts from the academia and industry are invited to present their work.
Symposium on AI in Digital Health
January 28, 2022
News and press releases