Machine Learning for Spatial Omics in Perturbed Systems

Florin Walter (Ph.D. Student)

Spatial omics technologies enable the study of omics modalities such as the genome, transcriptome, proteome or epigenome in their spatial context, thereby allowing a deeper understanding of cell function, cell communication or tissue development. Spatial technologies based on fluorescence imaging generate single-molecule resolution data and are therefore particularly useful for studying biological systems at the cellular or even subcellular level. This also allows spatial observation of the effects of molecular perturbations on individual cells. The goal of my PhD is to develop statistical and machine learning methods to better understand these effects and use them to infer biological networks.

Primary Host:	Oliver Stegle (German Cancer Research Center (DKFZ) & EMBL Heidelberg)
Exchange Host:	Christoph Bock (CeMM Research Center for Molecular Medicine)
PhD Duration:	01 July 2022 - 31 December 2025
Exchange Duration:	01 January 2024 - 30 June 2024 - Ongoing