Hauke Fischer

I am currently pursuing my PhD at TU Wien, focusing on the development of numerical tools and simulations for various experiments. My research aims to elucidate the implications of these experiments in the quest for new physics while optimizing their design.

One notable achievement involves leveraging machine learning algorithms to accelerate simulations of semiconductor chalcocite by a remarkable factor of approximately 100.000, enhancing our understanding of its phase transitions.

My expertise encompasses programming in Python and Mathematica, utilizing Linux Clusters (VCS), and analyzing and presenting simulation data. My interests lie at the intersection of applied mathematics and computer science, with a burgeoning enthusiasm for data science and machine learning.

Several years of experience in computational physics research: Development of simulation methods and appication of machine learning methods to speed of physical simulations

2 Bachelor degrees: Mathematics & Physics; 1 Master degree in Physics; soon a PhD in Physics