The Computational Cancer Biology lab led by Martin Schaefer is looking for a highly motivated and talented computational postdoctoral researcher. The postdoc will develop and apply network-based methods for omics data integration and to identify epigenetic cancer driver events.
Cancer is driven by alterations on all molecular levels. However, while our knowledge about the contribution of point mutations to the phenotypic hallmarks of cancer has advanced substantially over the last decade, we still need to solve the puzzle of how the numerous quantitative changes (eg epigenetic or translational) and copy number changes of larger genomic regions are related to cancer initiation and progression. Our lab employs methods from the fields of population genetics and network biology to address these questions.
The successful candidate will work together with other members of the lab as well as with wet lab collaborators to develop and employ network biology methods to a) model interactions between alterations on different molecular levels and b) systematically identify network level selection in cancer.
Candidates should hold a PhD (in a relevant area) and have expertise in statistics, programming, the analysis of cancer omics data and network biology. Knowledge of data science techniques such as machine learning and being familiar with concepts of tumor evolution are a plus.