• 🎓 5th ECMWF-ESA Machine Learning Workshop
  • 2026-04-13T11:00:00+02:00
  • 2026-04-17T15:00:00+02:00
  • The fifth edition of the ECMWF–ESA Workshop on Machine Learning for Earth Observation and Prediction will provide an up-to-date snapshot of the state of the art in this rapidly evolving field and a forum of discussion and interaction among worldwide scientists and practitioners about the current opportunities and challenges presented by the use of ML technologies for ESOP.
When

Apr 13, 2026 11:00 AM to Apr 17, 2026 03:00 PM
(Europe/Berlin / UTC200)

Where

Tecnopolo di Bologna, Via Stalingrado 84/3, 40128 Bologna

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Workshop motivation and goals

The use of Machine Learning (ML) technologies is becoming prevalent in an ever-growing number of applications in Earth System Observation and Prediction (ESOP). Additionally, the scale, complexity and sophistication of the ML technologies applied in ESOP has also increased considerably over the last few years, reflecting the growing uptake of ML ideas in the ESOP communities and benefiting from increased interest of ML domain scientists and of large commercial actors. This trend has reached the stage where end-to-end data-driven workflows are being considered for future operational weather and climate prediction systems. On the other hand, questions remain about the ability of purely data driven methods to advance the science underlying the slow but steady progress of ESOP capabilities based on physics-based algorithms.  

In parallel with the scientific developments brought about by ML, at the same time there is a shift in computational platforms for High Performance Computing (HPC) from CPU-based to GPU/TPU-based systems. ML applications benefit from this trend as they are typically developed on GPU/TPU machines, while the process of porting codes currently used for weather and climate prediction to the new computing architectures is laborious. Looking further ahead, even more disruptive technologies are appearing on the horizon, such as quantum, edge, or neuromorphic computing.  

Thematic areas

Contributions are welcome in all areas connected to the application of ML technologies to ESOP applications, with a particular focus on the thematic areas below.     

  • Hybrid ML-Physics based systems for Data Assimilation and Weather and Climate prediction 
  • End-to-end ML systems for Data Assimilation and Weather and Climate Prediction 
  • Machine Learning applications for Earth system observations 
  • High-performance and new computing technologies for ML applications in ESOP 
  • Machine Learning for Digital Twins of the Earth system 

Timeline

  • w/c 12 January 2026: Notification of Abstract acceptance 
  • 31 January 2026: Registration for in person participation closesÂ