At HIDSS4Health, we address diverse challenges in the areas of imaging & diagnostics, surgery & intervention, models for personalized medicine and public health. Find out more about our research activities.
The aim of the Helmholtz Information & Data Science School for Health (HIDSS4Health) is to attract, promote and train the best young talents at the interface between data science and health-related applications. HIDSS4Health offers a structured doctoral training program embedded in a highly interdisciplinary research environment, bringing together experts from the data and life sciences. The scientific curriculum is complemented by training measures that provide doctoral researchers with the key qualifications expected from future leaders in science and industry.
Imaging & Diagnostics
Imaging & Diagnostics
In Imaging & Diagnostics, we use machine or deep learning to exploit increasingly large and complex datasets generated by current high-throughput technologies in medicine, biology and health-related biotechnology. We need to deal with various challenges including real-time conditions, the quantification of uncertainty and ambiguity in imaging and omics data as well as the development of explicable decision-making.
Surgery & Intervention 4.0
Surgery & Intervention 4.0
In Surgery & Intervention 4.0, we focus on the role of data science in robot- and computer-assisted surgery and interventions. This includes the development and use of computational methods for planning and automation of examinations, surgery and interventions of different types and for intelligent assistive systems collaborating with the physicians, guiding them and supporting their learning process.
Models for Personalized Medicine
Models for Personalized Medicine
In Models for Personalized Medicine, we plan to integrate data-driven modeling, simulation, and visual exploration with first principles modeling. It includes models for real-time applications or patient models for interactive visualization. Data to be considered includes text data (such as intervention logs, admission notes), time series data, features extracted from images or omics data, as well as more traditional numerical data (e.g., lab results).
Public Health
Public Health
In Public Health, we leverage data science to better understand, predict, and improve health at the population level, spanning prevention, health services, and epidemiology. We develop and apply methods such as scalable data integration, machine learning, and spatio-temporal modeling to turn heterogeneous real-world data (e.g., routine care, registries, and surveillance data) into actionable evidence for decision-making in public health. We place strong emphasis on robustness, interpretability, and responsible use of data so that analytical results can support practical interventions and policy under real-world constraints.
Training program
Training program
The goal of the HIDSS4Health curriculum is that its doctoral researchers become experts in data science and in at least one health-related field. It will include the ability to communicate with experts from both sides and to transfer knowledge from data to life science and vice versa. All lectures, courses, retreats and summer schools are held in English.
Apply now
Are you curious about how we are actively pursuing this goal in practice? Learn more about the various projects taking place at our graduate school across the three partner institutions: KIT, DKFZ, and Heidelberg University.
