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Esther Rolf

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Postdoctoral Associate in Geospatial and Environmental Machine Learning, Neural Fields, and Earth Observation University of Colorado Boulder in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Postdoctoral Associate position with 2 years of guaranteed funding. Funded in part by Taylor Geospatial.

Deadline

Oct 30, 2027

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Country

United States

University

University of Colorado Boulder

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Keywords

Computer Science
Environmental Science
Biology
Remote Sensing
Earth Science
Earth Observation
Statistics

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About this position

University of Colorado Boulder’s Department of Computer Science is hiring a Postdoctoral Associate in the Geospatial and Environmental Machine Learning (GEM) Lab.

This postdoc sits at the intersection of neural fields, Earth observation, and geospatial machine learning, with a research agenda centered on building continuous, queryable, uncertainty-aware representations of the planet that can update as new data arrives. The role includes developing new neural field models and studying scaling laws for spatiotemporal models.

Research applications mentioned include ecological monitoring, natural hazard monitoring, and connections to Earth systems models. The project is funded in part by Taylor Geospatial and is described as part of a larger effort to create openly shared digital public goods, including hybrid neural fields, global-scale geospatial feature detection, and community benchmarks.

The position is in-person in Boulder, Colorado, United States, with a Winter/Spring 2027 start and 2 years of guaranteed funding. Strong candidates should have first-author publications at top CV/ML venues such as ICLR, ICML, CVPR, and AIStats, and should work well in interdisciplinary teams. Background in neural fields, remote sensing, or scaling laws is preferred.

Apply by October 30 for full consideration. The full position description is linked in the post.

Funding details

Postdoctoral Associate position with 2 years of guaranteed funding. Funded in part by Taylor Geospatial.

What's required

Strong candidates should have a track record of first-author publications at top CV/ML venues such as ICLR, ICML, CVPR, or AIStats, and be able to work effectively with interdisciplinary teams. Background in neural fields, remote sensing, or scaling laws is a plus.

How to apply

Review the full position description and apply through the linked jobs.colorado.edu posting. Submit by October 30th for full consideration, and use the posting contact/email link if you have questions.

More information can be found here

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