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Julija Zavadlav's research focuses on the development of efficient predictive computational methods and models in application areas ranging from life sciences to engineering. Her work integrates traditional physics-based approaches with emerging machine learning techniques and Bayesian modeling to develop novel concurrent multi-resolution simulation techniques. Zavadlav studied physics at the University of Ljubljana, where she received her Ph.D. in 2015 while working at the National Institute of Chemistry in Slovenia. In 2016, she joined the Computational Science and Engineering Laboratory at ETH Zurich, where she was awarded an ETH Postdoctoral Fellowship. By 2019, she was appointed as an Assistant Professor of Multiscale Modeling in Fluid Materials at the Technical University of Munich (TUM) and has since been recognized with an ERC Starting Grant in 2022. Her research interests include machine learning, Bayesian modeling, and advanced simulation techniques.
Technical University of Munich • Munich, Germany
Assistant Professor in Multiscale Modeling of Fluid Materials.
ETH Zurich • Zurich, Switzerland
Conducted research in Computational Science and Engineering as part of the Computational Science and Engineering Laboratory.