Postdoctoral Associate – Freshwater Nitrous Oxide Modeling

Cary Institute of Ecosystem Studies • Millbrook, NY

4d • Hybrid

About The Position

The Cary Institute of Ecosystem Studies invites applications for a postdoctoral associate in freshwater biogeochemistry and machine learning. The postdoc will collaborate on an NSF-funded project developing a knowledge-guided machine learning model to quantify global riverine nitrous oxide (N2O) emissions. The project combines process-based modeling with machine learning, and involves compiling and synthesizing global databases of riverine N2O measurements alongside large-scale geocomputation to upscale results across the global river network. The postdoc will have the opportunity to build interdisciplinary expertise spanning freshwater biogeochemistry, geospatial data science, and machine learning, and to contribute to research relevant to international climate assessments. Depending on interest and opportunity, there may be scope for the postdoc to engage in limited field-based research on riverine N2O dynamics, complementary to the core project. Funding for the position is secured for 2 years. The initial appointment is for one year, with possibility of extension based on performance. The anticipated start date can be as early as September 2026, with flexibility. The position is based at Cary Institute in the beautiful Hudson Valley of New York, a short way north of New York City. The position may have locational flexibility for exceptional candidates. Cary Institute is home to a diverse, vibrant, and supportive community of colleagues.

Requirements

• Expertise in freshwater or riverine biogeochemistry, particularly nitrogen cycling.
• Strong quantitative skills, including experience with process-based modeling and/or machine learning methods.
• Experience handling and synthesizing large, heterogenous environmental datasets.
• Familiarity with geospatial data science and large-scale geocomputation.
• A strong record of publishing peer-reviewed research.
• Self-motivated and comfortable working both independently and in close collaboration with the PI.
• Completed a PhD in a relevant field before the start date of this position.

Nice To Haves

• Limited field-based research on riverine N2O dynamics.

Responsibilities

• Design, plan, and execute scientific research in collaboration with the PI.
• Compile, harmonize, and synthesize a global database of riverine N2O measurements and predictor variables from published and contributed sources.
• Develop and calibrate a knowledge-guided machine learning framework and apply it via large-scale geocomputation across the global river network.
• Present research findings in peer-reviewed papers, at scientific meetings, and in other forums.

Benefits

• Fully benefitted

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree