“RESEARCH FOR A LIFE WITHOUT CANCER” IS OUR MISSION AT THE GERMAN CANCER RESEARCH CENTER. WE INVESTIGATE HOW CANCER DEVELOPS, IDENTIFY CANCER RISK FACTORS AND LOOK FOR NEW CANCER PREVENTION STRATEGIES. WE DEVELOP NEW METHODS WITH WHICH TUMORS CAN BE DIAGNOSED MORE PRECISELY AND CANCER PATIENTS CAN BE TREATED MORE SUCCESSFULLY. EVERY CONTRIBUTION COUNTS – WHETHER IN RESEARCH, ADMINISTRATION OR INFRASTRUCTURE. THIS IS WHAT MAKES OUR DAILY WORK SO MEANINGFUL AND EXCITING.

The Junior Research Group “Developmental Origins of Pediatric Cancer” is seeking from September 2025 a
Our lab studies how neurodevelopmental principles underlie pediatric cancer formation. We investigate the underlying genetic programs of normal development to dissect how differentiation goes wrong during tumorigenesis. We also investigate tumor-specific mechanisms of transformation and growth.
To understand these processes, the lab integrates single-cell sequencing, whole-genome sequencing, RNA sequencing, mouse models, iPSC-derived organoids, and functional genomics approaches. The lab has strong collaboration partner in computational biology and bioinformatics, and the candidate will be jointly supervised by the “Clinical Bioinformatics - Translational Genomics” group (Division of Pediatric Neurooncology). Through this multidisciplinary approach, the lab seeks to reveal how hijacking of developmental trajectories leads to pediatric brain cancers.

We are looking for a data engineer

• to generate and harmonize user-friendly, standardized data analysis pipelines for a variety of computational tasks, including, but not limited to, mutation calling, bulk and single-cell RNA-seq analysis, and ChIP-seq/Cut&Run datasets
• to perform large-scale data integration of single-cell RNA-seq datasets and generate user-friendly applications for data exploration
• to perform data analysis derived from diverse scientific topics in collaboration with PhD students and postdocs
• to train students and postdocs in basic data analysis pipeline implementation
• to explore pipelines for analyzing high-content image analysis.

Genomics approaches used in our laboratory include, but are not limited to, whole-genome sequencing, RNA-seq, ChIP-seq, Cut&Run, single-cell transcriptomics, and spatial omics. Imaging approaches include multiplexing confocal imaging and live-cell imaging.