AI and Machine Learning for Earth System Modeling and Prediction

About

The AI and Machine Learning for Earth System Modeling and Prediction summer school is designed for graduate students eager to explore the forefront of machine learning (ML) and artificial intelligence (AI) applications in Earth system science. With the exponential growth in climate data and advances in computational methods, this course offers students the opportunity to apply cutting-edge ML techniques to better understand, model, and predict the behavior of the Earth system.

Participants will gain hands-on experience with state-of-the-art AI and ML methods—ranging from deep learning and generative models to hybrid physical–ML approaches and uncertainty quantification—applied to the unique challenges of complex dynamical systems such as the atmosphere, ocean, and cryosphere.

Course leader

Aneesh Subramanian (University of Colorado Boulder); William Chapman (University of Colorado Boulder)

Target group

The Summer School is mainly geared towards Master’s and Ph.D. students, and postdocs in relevant fields (e.g., Mathematics or Climate Science with a strong interest in data science and machine learning).

Language of instruction: English.

Course aim

By the end of the course, participants will:
- Understand state-of-the-art ML/AI methods and how to tailor them for climate and Earth system applications
- Gain experience building, training, running, and validating ML models with real Earth science data
- Learn how the scientific community has integrated ML with physical models and interpret these results in a geoscientific context
- Collaborate with peers and mentors to solve practical problems at the intersection of AI and climate

Fee info