The German Cancer Research Center (DKFZ) is one of Europe’s largest cancer research centers. “Research for a life without cancer" is the mission of our world-class scientists and all our team members.
The DKFZ is a place where the brightest minds pursue bold ideas and seek answers to pioneering scientific questions through collaboration, innovation, and exploration across many disciplines. We provide a dynamic environment that empowers excellence with state-of-the-art technologies, cutting-edge infrastructure, and a global scientific network.
Contribute your knowledge, vision, and dedication to create a space where scientific discovery in cancer research is transformed into benefits for human health.
As a core partner in Helmholtz Imaging (HI), the German Cancer Research Center is committed to advancing imaging science and technology across all scales, modalities, and scientific domains together with the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC, Berlin), the Helmholtz-Centre for Environmental Research (UFZ, Leipzig), and the German Electron Synchrotron (DESY, Hamburg).
Within HI, DKFZ contributes a strong focus on AI-based image analysis and AI validation, offering an attractive research environment with excellent infrastructure, mentoring for young investigator group leaders, administrative support for extramural funding (e.g. ERC), and an international, collegial, and family-friendly atmosphere.
We are seeking, at the earliest possible date, an excellent researcher with an outstanding scientific record in machine learning and data-driven methods for imaging to fill a
Team Lead Position in Data-Centric AI
Reference number: 2026-0120
- Heidelberg
- Full-time
- Medical Image Computing
Your Tasks
The Young Investigator Group will complement DKFZ’s image analysis and AI methodology activities by focusing on data-centric AI and robust machine learning for imaging across domains, with applications spanning biomedical imaging and other imaging-intensive fields represented within the Helmholtz Association.
The group’s methodological focus will be on developing algorithms and frameworks that ensure stable and predictable model behavior under real-world variability and perturbations, such as changes in acquisition devices, experimental setups, environmental conditions, domains and populations, as well as noise characteristics and artifacts. Robustness is understood as a key building block toward trustworthy deployment of AI systems in high-stakes scientific, industrial and biomedical applications.
Potential research topics include:
- robustness to distribution shifts, domain adaptation, and domain generalization
- out-of-distribution detection and modeling
- uncertainty estimation
- innovative data curation approaches
- learning under label noise and weak supervision
- synthetic data generation
- fairness research
- active and interactive machine learning
- trustworthy AI validation
The group leader is expected to actively collaborate with HI support and scientific teams at DKFZ, DESY, UFZ and MDC, contributing to Helmholtz Imaging support activities, including direct interaction with Helmholtz researchers as well as the dissemination and reuse of HI solutions across the Helmholtz Association. The role includes contributions to training, knowledge transfer and education (e.g., seminars, lecture series, summer schools, workshops, and supervision of students and early-career researchers), as well as national and international representation of Helmholtz Imaging and DKFZ. In addition, the group leader is expected to contribute, together with other HI teams at DKFZ, to strategic initiatives and to the continuous shaping of the HI mission.
Your Profile
- A PhD in computer science, physics, mathematics, or a related discipline with less than 6 years of postdoctoral experience at the time of application (excluding child-rearing periods)
- Outstanding scientific record with deep expertise in machine learning, AI, computer vision, and data processing, with experience in collaborative software development
- Highly collaborative, open-science mindset, strong self-motivation, and interest in pursuing innovative research at the frontiers of machine learning
