The Glaciology and Machine Learning Summer School (GlaMacLeS) is an NSF-funded project aimed at building foundational skills and a community of practice for early-career researchers at the intersection of machine learning and cryospheric science. GlaMacLeS will take place at the Taft-Nicholson Center in Southwest Montana in the summer of 2026.

This course is intended to provide students with a comprehensive overview of the rapidly-changing interface of glaciology with machine learning and artificial intelligence. Key topics that may be covered include:

• An overview of machine learning methods for glaciologists
• Adjoint models and backpropagation
• Deep learning for glaciological remote sensing
• Gaussian process emulation for model prediction
• Deep learning-based emulation of ice physics

The course is primarily oriented towards researchers at all career stages that have strong foundational knowledge in glaciology, glaciological modeling, or remote sensing, and that wish to integrate ML methods into their work while simultaneously working to establish a community of practice.

The course will be held from 4th – 13th July 2026 and will include 4 days of interactive lectures by course instructors, 3 days of student project development in collaboration with course instructors, a one day excursion to nearby Yellowstone National Park, and two days for travel and orientation.

Fixed costs for attending the course (including airfare, transportation, and room and board at the Taft-Nicholson Center) are covered by a generous grant from the National Science Foundation.

The course is open to researchers (at any career stage) from anywhere in the world working in any facet of glaciology.

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How to Apply

Admission to GlaMacLeS is determined based on a competitive application process, with applications evaluated by an advisory board in late February 2026. Applications should be e-mailed to Doug Brinkerhoff (glamacles@mso.umt.edu) no later than 15th February 2026. All applicants will receive e-mail confirmation of their application within three days. Successful applicants will be notified by 1st March 2026. Because both available funding for participant support as well as physical space are limited, the course is limited to 15 students.

Applications must consist of the following three elements:

1. a cover letter including your name, university affiliation, position, email address, and a statement describing how participation in this course would be beneficial to your research and professional development. This letter should be limited to one page.
2. a curriculum vita
3. a description of your proposed thesis/primary research interests. This document does not need to include a full methodological description – just a brief overview of your work. In particular, emphasize how this research can utilize machine learning. This document should describe major collaborations included as part of this effort.

Application deadline: 15 February 2026

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_{Cover image by Bernd Dittrich.}