Pascal Salzbrenner

About

I am a researcher in the Future AI Infrastructure group. As part of an interdisciplinary team researching materials for cloud computing applications, I contribute my expertise in computational materials discovery at the atomistic level.

I have a PhD from the University of Cambridge, where I worked on techniques to accelerate high-throughput atomistic modelling. This included combining density functional theory and machine learning methods to uncover the phase diagrams of heavy metals and developing a new numerical integration technique for materials simulations. I then went to work for Ansys, now part of Synopsys, where I developed a machine-learning toolkit for training models to predict engineering-relevant properties.