March 16 – 20, 2026 | National Technical University of Athens (Greece)

The Joint Winter School on “AI for Earth System, Hazards & Climate Extremes” is a 5-day hands-on program hosted by the MeDiTwin and AI4PEX projects, bringing together leading experts and early-career researchers to explore the latest advances in artificial intelligence for extreme event analysis. The school offers a combination of theoretical lectures, hands-on training, and real-world applications, focusing on cutting-edge AI techniques to improve modelling, understanding, and impact assessment of Earth hazards and climate extremes.

Event Details

When: March 16 – 20, 2026
Location: National Technical University of Athens – Patission Campus (map)
Format: in-person (hands-on)
Working Language: English
Participant Fees: 350 €

Registration

Coming soon!

Who it’s for and why it matters

We welcome Master’s and PhD students, postdoctoral researchers, and early career professionals eager to explore the latest advances in AI, data analytics, and environmental sciences. Whether you come from academia, research, or industry, if you’re curious and ready to learn, this winter school is for you!

See full eligibility details in the call (PDF)

We will offer a limited number of scholarships to international students to attend our Joint Winter School. Selection for scholarships will prioritize diversity, equity, and inclusion (DEI), with a focus on applicants from low-income or widening countries.
We look forward to supporting you to join this exciting career advancement opportunity in Athens, Greece!

Join us to:

Apply AI to Climate Extremes
Use AI tools and methods to model, monitor, and predict extreme weather and climate events in real-world scenarios.
Understand and Explain Models
Learn how to make AI models more transparent, physics-informed, and causally grounded to improve trust and understanding.
Assess and Communicate Results
Develop skills to interpret results and communicate findings effectively to scientists, decision-makers, stakeholders, and the public.
Collaborate and Exchange Ideas
Work with experts in AI and climate science and connect with other researchers from different backgrounds.

Key Pillars

The foundation of all analyses, focused on the detection, harmonization, and integration of diverse data sources, from in-situ measurements and remote sensing observations to derived products, to ensure accuracy, consistency, and reliability across applications.

Development of models for monitoring, forecasting, and impact assessment across spatial and temporal scales, combining physical principles with statistical and machine learning methods to capture complex Earth system dynamics and improve predictive performance.

Advancement of explainability, causal reasoning, and attribution to identify drivers of change and assess impacts, combined with uncertainty quantification and robustness analysis to ensure transparent and reliable outcomes.

Bridge between science and action, using AI tools such as large language models and digital twins to support operationalization, policy development, informed decision-making, and responsible risk communication.