Fedor Scholz is a researcher with a decade of experience at the crossroads of machine learning, computer science, and cognitive science, currently based at the University of Stuttgart. During his PhD at the University of Tübingen and IMPRS-IS he developed ML methods for river discharge prediction and now focuses on applying deep generative models to improve geoscientific modeling and connect data-driven approaches with physical understanding. He combines practical software engineering experience from industry roles with rigorous academic training from KIT and Tübingen, enabling him to translate complex scientific problems into robust modeling solutions. Notably, his work blends cognitive-science-inspired perspectives on modeling with state-of-the-art deep learning, aiming to make physical system representations both more expressive and scientifically interpretable.
10 years of coding experience
5 years of employment as a software developer
Master of Science - MS Cognitive Science, Master of Science - MS Cognitive Science at University of Tübingen
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Karlsruhe Institute of Technology (KIT)
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Fedor Scholz - Researcher at University of Stuttgart