CR
Cryospheric Sciences
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Julia Kaltenborn

Julia Kaltenborn is a PhD student at McGill University and the Mila - Quebec AI Institute supervised by David Rolnick, working on advancing deep learning-based emulators for Earth systems. She is contributing to the AI4Snow (https://ai4snow.eoc.dlr.de/) and the Terrestrial Snow Mass Mission project (https://spaceq.ca/canadian-space-agency-issues-call-for-ideas-for-synthetic-aperture-radar-concept-focused-on-terrestrial-snow-mass/) with the goal to improve snow water equivalent prediction with machine learning.

Did you know? Machine learning can help us understand the cryosphere!

A bar chart showing in which years how many publications included machine learning.

    Recently, Machine Learning (ML) has emerged as a powerful tool within cryospheric sciences, offering innovative and effective solutions for observing, modelling and understanding the frozen regions of the Earth. From learning snowfall patterns and predicting avalanche dynamics to speeding up the process of modelling ice sheets, ML has transformed cryospheric sciences and bears many o ...[Read More]