Michael Habeck is a professor and experienced researcher in probabilistic machine learning with 15 years of scientific and leadership roles across top European institutes. He focuses on Bayesian inference, Markov chain Monte Carlo and probabilistic modeling, translating rigorous biophysics training (PhD) into advanced statistical methods for complex biological data. His career includes leadership positions at the Max Planck Institute and an Emmy Noether group, and he currently holds a professorship at Universitätsklinikum Jena. Michael bridges deep theoretical insight with practical research engineering, having worked in both academic labs (EMBL, Institut Pasteur) and industry as a software engineer early in his career. Based in Göttingen, he combines a physicist’s precision with a computational scientist’s toolkit to tackle inference challenges in biophysics and beyond. Colleagues value his ability to lead groups while staying hands-on with probabilistic algorithms and experimental collaborations.
15 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Biophysics, Doctor of Philosophy (Ph.D.), Biophysics at University of Regensburg
Master’s Degree, Physics, Master’s Degree, Physics at Heidelberg University
Contributions:62 commits, 43 pushes, 1 branch in 2 years 3 months
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Michael Habeck - Professor at Universitätsklinikum Jena