Summer school on Machine Learning for Space 2026

8th to 12th June 2026 – 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.

Please note that the Summer School does not offer fellowships or financial support. Participants are responsible for their own travel and accommodation costs. The summer school does not include lunch or dinner. Participants are expected to arrange and pay for their own meals.

🎯Learning Objectives

By participating in the Summer School, attendees will be able to:

1. Explain core concepts in space physics and identify key machine-learning applications in the field.
2. Locate, access, and preprocess datasets from major missions such as Magnetospheric Multiscale Mission, Cluster, THEMIS, Solar and Heliospheric Observatory, Solar Dynamics Observatory, Solar Orbiter, and OMNI, applying workflows for cleaning, normalization, feature extraction, and time alignment.
3. Apply and interpret dimensionality-reduction and unsupervised techniques (e.g., PCA, clustering, covariance analysis) to uncover structure and regimes in space-physics data.
4. Design, implement, and validate machine-learning models for classification, prediction, and event detection in space-physics applications.
5. Critically assess model performance, limitations, uncertainty, and physical interpretability, and discuss the role of ML in current and future space missions.
6. Demonstrate proficiency with Python-based scientific and ML tools, such as scikit-learn, Pandas, and PyTorch.

Prerequisites

Basic Python programming and introductory linear algebra are recommended. No prior machine-learning expertise is required.

Who should apply:
This summer school is designed for graduate students and early-career researchers who are studying or conducting research in space physics, space weather, or related areas. As well as those with a machine learning background who are beginning to apply their skills in these fields. 

Participation in the summer school is free of charge, but registration is mandatory and subject to approval by the organizer. 

Sign-up using the form: https://forms.gle/6n79Vdw2Nxxm3BPF9

The sign-up will close on the  12th of April, 2026.