Jan Gleixner is a computational biologist and postdoctoral researcher based in Heidelberg with 12 years of experience applying statistics and machine learning to biological data. Trained in molecular biotechnology (MSc 1.1) and now holding a PhD in biosciences, he has blended method development—ranging from causal inference hypothesis tests to neural-network segmentation of EM brain images—with applied computational biology across institutes like DKFZ, EMBL and Max Planck. His work spans algorithm design, reproducible analysis pipelines and GPU porting, and he has a track record of translating advanced statistical ideas into robust tools used in cancer and neurobiology research. A former iGEM winner, he combines hands-on lab experience with strong quantitative coding skills, making him effective at bridging wet-lab questions and computational solutions.
12 years of coding experience
Doctor of Philosophy - PhD, Biosciences, 1.0 (Thesis written and defended, title not yet awarded), Doctor of Philosophy - PhD, Biosciences, 1.0 (Thesis written and defended, title not yet awarded) at Heidelberg University
Master of Science - MS, molecular Biotechnology, 1.1, Master of Science - MS, molecular Biotechnology, 1.1 at Ruprecht-Karls-Universität Heidelberg
Contributions:4 pushes, 1 comment, 3 issues in 6 years
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