University of Colorado Boulder
1 week ago
Postdoctoral Associate in GeoAI, Geospatial Implicit Neural Representations, and Earth Observation Data University of Colorado Boulder in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Postdoctoral position with 2 years of guaranteed funding; extensions may be considered in the second year. Salary range is $85,000-$90,000 annually. Benefits include medical, dental, retirement plans, paid time off, tuition assistance for employee and dependents, and transit pass access.
Deadline
Oct 30, 2026
Country
United States
University
University of Colorado Boulder
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About this position
The University of Colorado Boulder is hiring a Postdoctoral Associate in the Geospatial and Environmental Machine Learning (GEM) Lab led by Professor Esther Rolf. This postdoc will join a multi-institution project on GeoAI, geospatial implicit neural representations (GeoINRs), and next-generation neural-field representations of Earth observation data.
The research aims to build continuous, queryable representations of the planet from multiple modalities of Earth data, support updates as new observations arrive, and provide calibrated uncertainty over user-specified regions and time ranges. The role also includes work on scaling laws for spatiotemporal datasets and integrating implicit neural representations with physical process-based models for more robust environmental monitoring and trusted GeoAI systems.
This is an in-person position based in Boulder, Colorado, United States. The postdoc will collaborate with a cross-institution team including researchers from Microsoft AI for Good, Washington University in St. Louis, and other partners. Visa sponsorship is not available.
Funding: 2 years of guaranteed funding, with possible extension in the second year. Salary is listed at $85,000-$90,000 per year. Benefits include medical, dental, retirement plans, paid time off, tuition assistance, and transit support.
Eligibility highlights: PhD in Computer Science or a closely related field (completed or expected by start date); strong publication record in top machine learning/computer vision venues; strong Python and PyTorch skills; mathematical maturity in linear algebra, probability, and optimization; excellent communication skills. Preferred experience includes neural fields, remote sensing, uncertainty quantification, scaling laws, open-source software, and multi-institution collaboration.
How to apply: Submit a CV, cover letter, and a 1-page research goals statement through CU Boulder Jobs. During the application, provide three references with names and email addresses. Apply by 2026-10-30 for full consideration.
Funding details
Postdoctoral position with 2 years of guaranteed funding; extensions may be considered in the second year. Salary range is $85,000-$90,000 annually. Benefits include medical, dental, retirement plans, paid time off, tuition assistance for employee and dependents, and transit pass access.
What's required
PhD already completed or expected by the start date in Computer Science or a closely related field; demonstrated first-author publications at top-tier computer vision and machine learning venues such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, or AISTATS; strong Python programming skills and hands-on experience with PyTorch or a similar framework; mathematical maturity in linear algebra, probability, and optimization; clear written and verbal communication skills. Preferred experience includes implicit/neural-field representations, geospatial or remote-sensing machine learning, large-scale training and inference pipelines, uncertainty quantification, scaling laws in machine learning, open-source research artifacts, and multi-institutional or multidisciplinary teamwork.
How to apply
Apply through CU Boulder Jobs with a CV, cover letter, and a 1-page research goals statement. If selected as finalist, degree verification will be handled by CU Boulder HR; submit translated transcripts only if your degree was earned outside the United States. Provide three references' names and email addresses during the application process. Do not email materials directly.
More information can be found here
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