The Computational Cancer Biology lab led by Martin Schaefer is looking to fill a computational position in the lab. The successful candidate will work on the development of methods for cancer driver gene detection.
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.
We are looking for a candidate holding at least a BSc. or MSc. in a Bioinformatics-related area. The candidate should bring experience in the analysis of NGS data and programming skills in R and python (or equivalent). Knowledge of data science techniques such as machine learning and being familiar with concepts of tumor evolution are a plus.