Nathan Jacobs
3 months ago
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Postdoctoral Associate in Hybrid Neural Fields for Earth Observation Washington University in St. Louis in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Full-time postdoctoral position with benefits eligibility. Funded by Taylor Geospatial. Two years of guaranteed funding with the possibility of extensions. Salary is competitive and commensurate with experience.
Deadline
Expired
Country
United States
University
Washington University in St. Louis
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About this position
Washington University in St. Louis’s Multimodal Vision Research Lab (MVRL) is hiring a Postdoctoral Associate in Hybrid Neural Fields for Earth Observation. The project sits at the intersection of computer vision, remote sensing, Earth observation, multimodal learning, and geospatial AI, with a research goal of building continuous, queryable, uncertainty-aware representations of the planet that can be updated as new observations arrive.
The postdoc will work with Nathan Jacobs at Washington University in St. Louis and collaborate with a cross-institution team including Hamed Alemohammad, Isaac Corley, Hannah Kerner, Konstantin Klemmer, Caleb Robinson, Esther Rolf, Marc Rußwurm, and Evan Shelhamer. Research themes include hybrid neural fields, global-scale geospatial feature detection, open-world mapping, natural hazard monitoring, ecological monitoring, and community benchmarks / digital public goods.
This is an in-person role based in St. Louis, starting Summer 2026 (or later). The position is full-time, benefits eligible, and comes with two years of guaranteed funding with possible extensions. Salary is competitive and commensurate with experience.
Applicants should have a PhD (or expect to complete one by the start date) in Computer Science, Electrical Engineering, or a closely related field. Strong evidence of research excellence is expected, especially first-author publications at top CV/ML venues such as CVPR, NeurIPS, ICLR, ICCV, or ECCV. Strong Python and PyTorch skills are required, along with mathematical maturity in linear algebra, probability, and optimization. Prior work in neural fields, remote sensing, uncertainty quantification, large-scale training/inference, or open-source research is a plus.
Applications are reviewed rolling until filled. To apply, submit the application form with your CV, a short statement describing how your prior work connects to neural fields and/or geospatial AI, and links to representative publications.
Funding details
Full-time postdoctoral position with benefits eligibility. Funded by Taylor Geospatial. Two years of guaranteed funding with the possibility of extensions. Salary is competitive and commensurate with experience.
What's required
PhD (or expected by the start date) in Computer Science, Electrical Engineering, or a closely related field. Strong candidates should have first-author publications at top-tier computer vision and machine learning venues such as CVPR, NeurIPS, ICLR, ICCV, or ECCV. Applicants should have strong Python programming skills, hands-on experience with PyTorch or a similar framework, mathematical maturity in linear algebra, probability, and optimization, and clear written and oral communication. Prior experience with implicit/neural-field representations, geospatial or remote-sensing machine learning, uncertainty quantification, large-scale training/inference pipelines, or open-source research artifacts is preferred.
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