Prof. Dr. Julija Zavadlav
Academic Career and Research Areas
The research area of Prof. Zavadlav (b. 1987) lies in the development of efficient and predictive computational methods and models for application areas ranging from life sciences to engineering. To this end, she is integrating traditional physics-based approaches with emerging machine learning techniques and Bayesian modeling and developing novel concurrent multi-resolution simulation techniques.
Prof. 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 and was awarded the ETH Postdoctoral Fellowship. In 2019, she was appointed as Assistant Professor for Multiscale Modeling of Fluid Materials at TUM. She was awarded with the ERC Starting Grant in 2022.
Awards
- ETH Postdoctoral Fellowship
- (1st place) Goldene Lehre Award 2022 for best lecture in Mechanical Engineering M.Sc. studies
- ERC Starting Grant 2022 (SupraModel)
Key Publications
Röcken, Sebastien; Burnet, Anton F.; Zavadlav, Julija: Predicting solvation free energies with an implicit solvent machine learning potential. The Journal of Chemical Physics 161 (23), 2024.
AbstractThaler, Stephan; Mayr, Felix; Thomas, Siby; Gagliardi, Alessio; Zavadlav, Julija: Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo. npj Computational Materials 10 (1), 2024.
AbstractThaler, Stephan; Doehner, Gregor; Zavadlav, Julija: Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls. Journal of Chemical Theory and Computation 19 (14), 2023, 4520–4532.
AbstractThaler, Stephan; Zavadlav, Julija: Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting. Nature Communications 12, 6884, 2021.
AbstractZavadlav, Julija; Melo, Manuel Nuno; Marrink, Siewert J.; Praprotnik, Matej: Adaptive resolution simulation of an atomistic protein in MARTINI water. The Journal of Chemical Physics 140 (5), 2014, 054114.
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