In the context of PrECISE Roland is developing computational methods to apply risk-stratification of prostate-cancer patients and to inform clinicians about optimal targeted treatment strategies using machine and deep learning strategies. To improve the understanding of disease onset, development and assess tumor hetereogeneity he combines next generation molecular data with clinical data, public databases and unstructured text.
Roland received his B.Sc. degree in Computer Science from the University of Applied Sciences Zurich. He was working as a Systems and Network Engineer with a focus on Cyber Security and as a Release Engineer for a software startup. He then moved into life sciences and received a M.Sc in Computational Biology and Bioinformatics from ETH Zurich. He did his Ph.D. at ETH Zurich and Eawag conducting research in the area of phenotypic heterogeneity and cellular memory in bacteria.