Programm overview
The DZD Health Data Scientist & AI Certificate Program (DZD HeaDS) is a two-year advanced training initiative designed to attract STEM students and early-career doctoral researchers to data-driven health research. The program is coordinated by the German Center for Diabetes Research (DZD) and integrates participants into an interdisciplinary network of experts in health research in general as well as diabetes and metabolism research in particular. Participants will gain hands-on experience in health data collection, standardization, analysis, and innovation through a modular curriculum and collaborative projects.
Target Group and Duration
The program is aimed at Master and PhD students in Europe from non-medical STEM disciplines who possess basic knowledge in information technology and programming. The modules run over a time course of 2 years, starting in May 2026.
The program is structured into:
- A Module: 3 introductory modules (Refresher courses can be elected according to one’s own prior knowledge)
- B Module: 5 obligatory modules
- C Module: 2 accompanying optional modules
Each introductory and obligatory module has a duration of approximately 2 full working days. Detailed content can be obtained from the module overview section below.
Selection
The program will admit 30 trainees. Participants will be selected by an interdisciplinary selection committee according to their convincing outline what drives them into health data research and how they will benefit from the program. Incomplete applications cannot be considered. We are aiming at a balanced group composition in terms of gender and diverse academic backgrounds.
Admissions will be announced in February 2026.
A-1
Kick off workshop: Get to know peers and research topics
A-1
Kick off workshop: Get to know peers and research topics
A-2
Refresher: Concepts of translational diabetes research
A-2
Refresher: Concepts of translational diabetes research
Date: 22-25 June 2026, 9:00 - 11.30am | Location: webinars - optional
Recommended for participants with little or no prior knowledge in health research. Topics will be basic anatomical and physiological principles of human bodies, pathogenesis of selected diseases, concept of precision medicine and basics of clinical study design.
A-3
Refresher: Current IT approaches in data-driven health research
A-3
Refresher: Current IT approaches in data-driven health research
Date: July 2026 | Location: webinars - optional
Essential for participants with little or no prior knowledge in computer science, recommended for all. The course refreshes knowledge in machine learning and deep learning. Introduction to standards of modern programming style (GitHub, version control, open source etc.).
B-1
Data collection in health research
B-1
Data collection in health research
B-2
Data standardization and data engineering
B-2
Data standardization and data engineering
B-3
Data analysis
B-3
Data analysis
B-4
Data-driven innovation in health research
B-4
Data-driven innovation in health research
B-5
Final event – Hackathon
B-5
Final event – Hackathon
C-1
Online lecture series: Health data – from science and care
C-1
Online lecture series: Health data – from science and care
Prof. Dr. Dr. h.c. mult. Martin Hrabě de Angelis is a leading geneticist specializing in genetics, diabetes, and systematic phenotyping. He earned his PhD in biology from Philipps University Marburg and completed postdoctoral research at The Jackson Laboratory (USA) on Delta/Notch signaling and mouse development. Since 2003, he has chaired Experimental Genetics at TUM and directs the Institute of Experimental Genetics at Helmholtz Munich, where he founded the German Mouse Clinic—the world’s first comprehensive mouse phenotyping center. His research has advanced understanding of the genetic and epigenetic basis of obesity and diabetes. Prof. Hrabě de Angelis is acting CEO of Helmholtz Munich, co-founder and board member of the German Center for Diabetes Research (DZD), and an elected member of the Leopoldina. He actively serves on committees such as the M1 Munich Medicine Alliance, the Coordination Group of Health Research Data Infrastructures (GFDI), and the Health Research Forum of the BMFTR advocating for better health data infrastructures in Germany. His work has been cited over 52,000 times, reflecting his global impact in translational biomedical research.
Dr. Steffen Schneider is an award-winning researcher in artificial intelligence and neuroscience, named Early Career Scientist of the Year 2024 by Academics for his scientific achievements and commitment to educational equity. He leads the Dynamical Inference Lab, developing machine learning algorithms to model dynamic biological processes and advance medical research. During his PhD within the European AI network ELLIS, he created the CEBRA algorithm for neural data analysis at EPFL and the University of Tübingen. Beyond research, he promotes AI education through his initiative KI macht Schule, offering courses, training, and open-access materials to thousands of students and teachers. His contributions have earned multiple honors, including the Wolfgang Heilmann Prize.
Prof. Reiner Jumpertz-von Schwartzenberg is a clinician-scientist specializing in clinical metabolism, obesity, diabetes, and gut microbiome research. He studied medicine in Bochum and Leipzig, earned his doctorate in 2012, and trained at Charité Berlin, becoming a board-certified internist, endocrinologist, and diabetologist. He gained international experience as a postdoctoral fellow at the NIH and UCSF, focusing on human metabolism, insulin resistance, and translational microbiome research. Since 2022, he holds the W3 Professorship for Clinical Metabolism and Obesity Research at the University of Tübingen in conjunction with Helmholtz Munich. He heads the Clinical Study Center for Diabetology and serves as senior physician at University Hospital Tübingen, leading multicenter studies on metabolic testing, imaging, and biomarkers. Additionally, he directs a Helmholtz Young Investigator Group and a Junior Research Group in the CMFI Excellence Cluster, investigating microbial metabolites in diabetes. Prof. Jumpertz-von Schwartzenberg is an active member of national and international professional societies, including the German Diabetes Society, German Obesity Society, EASD, and ADA.
Dr. Lars Oest leads the Data Management team at the German Center for Diabetes Research (DZD), ensuring research data infrastructures and services comply with FAIR principles. He brings extensive expertise in data science and engineering from previous roles as Head of AI Analytics at accantec group and Senior Consultant at BIVAL GmbH, where he managed projects in data pipelines, warehouses, and analytics. Dr. Oest earned his PhD in Computer Engineering from TU Hamburg, focusing on image and parallel processing for process control, and has delivered over 100 data analytics workshops for academic and industrial audiences. He is deeply involved in national research infrastructures (DZG, NUM/MII) and regulatory frameworks (GDNG, EHDS), making him a key expert in bridging data management and research compliance.
Dr. Sebastian Lobentanzer is a biomedical researcher and research software engineer with expertise in systems pharmacology and computational biology. He earned his PhD in pharmacology and toxicology and focuses on uncovering causal relationships in molecular biology. From 2021 to 2025, he worked as a postdoctoral researcher in Julio Saez-Rodriguez’s lab at Heidelberg University Hospital and, since 2024, collaborates with the Open Targets group at EMBL-EBI. In 2025, he became Principal Investigator at Helmholtz Munich and leads the Computational Biology Unit at the German Center for Diabetes Research (DZD), driving Accessible Biomedical AI Research (slolab.ai).
Sebastian bridges biomedical science and information technology, advocating open science and reproducibility. He leads the development of BioCypher, an open-source ecosystem for automating biomedical knowledge management (biocypher.org), and integrates large language models into biomedical workflows to enhance AI-readiness in life sciences.
