Scarlet Stadtler
Scarlet Stadtler is a trained meteorologist who focused on atmospheric chemistry during her doctoral thesis. Currently she works as a postdoctoral researcher at the Jülich Supercomputing Centre.
This course is designed to teach students about advanced and explainable machine learning methods in environmental science. The course focuses on introducing machine learning methods tailored to earth science and provides students with the skills to explain shallow neural networks, random forest, and UNet. In addition, the course provides an introduction to self-supervised learning and physically consistent transformations for multi-channel remote sensing data. By the end of the course, students will have a solid understanding of advanced machine learning methods and their applications in environmental science. They will have the skills to explain shallow neural networks, random forest, and UNet, and understand the importance of self-supervised learning in machine learning. Students will also have gained experience in using physically consistent transformations for multi-channel remote sensing data, which are essential for accurate analysis of environmental data.
Scarlet Stadtler is a trained meteorologist who focused on atmospheric chemistry during her doctoral thesis. Currently she works as a postdoctoral researcher at the Jülich Supercomputing Centre.
Timo Stomberg is a trained physicist and is currently working on his doctoral thesis at the University of Bonn within the KISTE project.
Ankit Patnala is a trained computational scientist and is currently working on his doctoral thesis at the University of Bonn and Jülich Supercomputing Centre within the KISTE project.