Michael Scherbela joined the research network as a PhD student in autumn 2020 and studied physics at the Graz University of Technology, specializing in computational physics. He is currently pursuing his PhD in mathematics, primarily conducting research on applications of deep learning methods to problems in the natural sciences. In particular he is interested in developing neural-network-based methods to find solutions to the Schrödinger equation, which lies at the heart of computational chemistry and quantum physics. To tackle this problem he combines existing quantum chemistry approaches with deep-neural networks, designs new network architectures for unsupervised learning, and optimizes existing approaches.

We are very happy to have Michael at our research network!