Jan Gleixner | PhD Candidate
Jan Gleixner is a passionate researcher with a keen interest in machine learning approaches to causality, particularly in the context of gene regulation. He earned his degree in Molecular Biotechnology from Heidelberg University, gaining diverse practical experience in optogenetics, gene therapy, protein stability, protein-protein interactions, and machine learning for computer vision in neurobiology. Jan also developed bootstrap-based tests for assessing total causal effects. His scientific journey includes notable participation in the iGEM competition, which sparked his lasting fascination with synthetic biology.
Jan’s strong programming skills, cultivated since high school, have been integral to his research successes. He is currently pursuing a PhD, working collaboratively with Oliver Stegle’s group at EMBL and Michael Boutros at DKFZ, where he is advancing both wet lab and computational techniques to map genotype to phenotype using pooled single-cell experiments.