Explore projects

Interactive exploration and analysis of large amounts of data from scientific simulations, in-situ visualization and application control are convincing scenarios for explorative sciences. Based on the open source software JupyterLab, a way has been a

Course material repository for the PRACE course "High Performance Scientific Computing in C++", 21 - 24 June 2021.

Benchmark data set for mapping from meta data to air quality metrics

Root count challenge

Jupyter notebooks to demonstrate the use of wavelets for image processing & feature detection

Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

PRACE Workshop - Interactive High-Performance Computing with Jupyter

Course material for the course "Programming in C++", 9 --13 May 2022, organized by the Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany.

Mapping of ozone data with machine learning methods.

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.