Doctoral candidate sub auspiciis Michael Scherbela is a mathematician and physicist. His key research area is the interface between mathematics and machine learning, with a particular emphasis on the development of methods for calculating wave functions using neural networks. As part of his doctoral thesis, Scherbela developed the DeepErwin code base – a framework that combines deep learning and variational Monte Carlo methods to calculate the energies of small molecules with high precision. After completing his master's programme in physics with a focus on computer-aided methods at Graz University of Technology, Scherbela earned his doctorate under Professor Philipp Grohs at the Faculty of Mathematics at the University of Vienna. He worked as a consultant at McKinsey & Co. and is currently working at Isomorphic Labs on the development of deep learning methods for drug development.

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