An online course on the principles of Simulation-Based Inference (SBI) will be organized by the Jülich Supercomputing Center on 7-8 September 2026.
This tutorial introduces SBI, a framework combining Bayesian modeling, AI techniques, and high-performance computing to address key challenges, su...
The Jülich Supercomputing Center (JSC) has announced a professional three-day online training course designed to empower researchers and developers in the field of Artificial Intelligence. Scheduled for 8–10 September 2026, the workshop aims to provide participants with the essential technical f...
An online 5-day course that combines lectures and hands-on sessions on Python for High-Performance Computing (HPC) will be organized by the Jülich Supercomputing Centre (Forschungszentrum Jülich, JSC) from 15 to 19 June 2026.
Python is increasingly used in high-performance computing projects....
An online 3 half-day training on the Unreal Engine and its use in science will be organized by the Jülich Supercomputing Center (Forschungszentrum Jülich) on 11-15 May 2026.
The Unreal Engine is one of the state-of-the-art 3D engines, which is mainly used for game development. In recent year...
The Jülich Supercomputing Centre will organize an online course on high-performance computing (HPC) with JupyterLab on 28 and 29 April 2026.
Open-source software such as Jupyter or JupyterLab combines interactive and reproducible computing while meeting the challenges of supporting a wide ra...
An online training on Data Analysis and Plotting in Python with Pandas will be organized by the Jülich Supercomputing Centre on 30 March 2026. This half-day training will introduce Pandas through simple, hands-on exercises, highlighting the capabilities of the software package step by step, star...
An online course on Advanced Parallel Programming with MPI and OpenMP, organized by the Jülich Supercomputing Centre in collaboration with the High-Performance Computing Center Stuttgart, will be held from 1 to 4 December 2025.
Centered on advanced programming with MPI and OpenMP, this cours...
The Jülich Supercomputing Centre (JSC) is organizing OpenStack training on 18 February 2025. This course aims to provide users of the HDFCloud or JUSUF Cloud with an introduction to various core OpenStack services. Participants will learn how to interact with these services and perform ope...
The Jülich Supercomputing Centre and High-Performance Computing Center Stuttgart (HLRS) will co-host an online training course on parallel programming using Message Passing Interface (MPI) and OpenMP from 2 to 5 December 2024. Registration is open until 17 November 2024.
The course will...
The Jülich Supercomputing Centre (JSC) is hosting an online two-day course titled Getting Started with AI on Supercomputers. The course will take place on 12-13 December 2023. The streaming platform link will be shared only with registered participants. To participate, please register before 5 ...
The Jülich Supercomputing Centre and High-Performance Computing Center Stuttgart (HLRS) will jointly hold an online training course on parallel programming with Message Passing Interface (MPI) and OpenMP from 27 to 30 November 2023. Registration is open until 17 November 2023.
The course w...
The Jülich Supercomputing Centre is organizing a training course on Introduction to Explainable Deep Learning on Supercomputers from 4 to 6 December 2023. The course will be conducted online, and the link to the streaming platform will be provided to registered participants only. Please complete...
The MATLAB for HPC workshop at Jülich Supercomputing Center (JSC) is designed to teach you about parallel computing using large computing resources. During the workshop, you will learn how to effectively use MATLAB and MATLAB Parallel Server to speed up your computations on the JSC cluster throu...
MathWorks and Jülich Supercomputing Center (JSC) are organizing a hands-on workshop titled Parallel Computing with MATLAB on 24 October 2023, from 2:00 PM to 5:00 PM CEST. This workshop is for PhD students, researchers, and early career scientists who want to optimize their code for compute cor...