Date and time: 8-12 June 2026
Where: KTH, Stockholm, Sweden
This one-week summer school introduces modern machine learning (ML) methods applied to heliophysics and magnetospheric science, aimed at graduate students and early-career researchers. The program combines foundational lectures with hands-on coding sessions, enabling participants to build both conceptual understanding and practical skills.
Participants will explore core ML techniques—including supervised and unsupervised learning methods—and learn how to access, preprocess, and analyze real datasets from major space missions. Guided by experienced mentors, attendees will work directly with mission data, develop reproducible workflows, and implement ML models relevant to current research challenges in space physics.
The summer school emphasizes interactive learning, collaborative problem-solving, and discussion of best practices in scientific machine learning. At the end of the week, participants will have the opportunity to present their work or research interests in short lightning talks presentations, providing a platform for feedback, discussion, and networking.
Information
For information on Program, Venue, Contact details please go to the website of ASAP – Summer school on Machine Learning for Space 2026.
Feature image – Credit: ESA/ATG medialab
Co-sponsored by Digital Futures
