During the Summer School, students will have the exciting opportunity to collaborate on group projects, with a maximum group size of 5-6 people. The projects will intend to utilize high performance computing resources to train and evaluate latest machine learning approaches on challenging data sets from the Earth and Climate Sciences.
 
The idea behind the projects is highly challenging: students are tasked with proposing and implementing modifications to known algorithms and solutions on a provided dataset in collaboration with their challenge leader. The top three projects selected by a jury will be awarded with the best-project-award, accompanied by a formal certificate and recognition from the ELLIS summer school.

Projects will come from one of four areas:
A) Hybrid Modeling
B) Robustness (incl. uncertainty, cross-validation, probabilistic modeling)
C) Interpretability
D) Large-scale deep learning