Analyzing Materials through Machine Learning
Vincent Stimper (Ph.D. Student)
Measurement techniques to characterize the properties of materials, such as photoemission spectroscopy, were continuously improved throughout the last decades. This led to a drastic increase of the measured data in terms of size, resolution, and complexity. Analyzing those dataset poses challenges, e.g. processing them in an efficient manner and extracting physically meaningful and relevant parameter. In my PhD, I want to tackle them by using and extending existing machine learning models and developing new approaches.
|Primary Host:||Bernhard Schölkopf (ELLIS Institute Tübingen & Max Planck Institute for Intelligent Systems)|
|Exchange Host:||José Miguel Hernández-Lobato (University of Cambridge)|
|PhD Duration:||01 January 2020 - Ongoing|
|Exchange Duration:||01 January 2020 - 31 December 2020 - Ongoing|