Postdoc: Remote Sensing & AI for Forest Carbon Change

NASA Jet Propulsion Laboratory; Pasadena (CA); On-site; USD 75,000 - 92,000; Full time

Job summary

NASA Jet Propulsion Laboratory (JPL) in Pasadena, CA, invites applicants for a senior research role focusing on machine learning and geospatial analysis of forest biomass change. The successful candidate will develop algorithms, work with SAR/LiDAR data, and lead publications in collaboration with interdisciplinary teams.

The role requires a PhD or near completion and strong programming skills in Python, MATLAB, or R, with a track record of quantitative research and peer‑reviewed work.

Qualifications

• PhD (completed or near completion) in remote sensing, computer science, environmental science/engineering, statistics/mathematics, ecology, geography, or related field with forest applications.
• Experience with quantitative geospatial and satellite data analysis, including SAR and/or LiDAR remote sensing.
• Strong skills in advanced machine learning (time-series; generative/foundation AI) and data assimilation or ecosystem-model coupling.
• Proficiency in Python, MATLAB, or R and solid statistical background.
• Track record of quantitative research and peer‑reviewed publications; ability to work in interdisciplinary teams.

Responsibilities

• Lead independent research within a collaborative team using machine learning to map biomass change.
• Perform geospatial analyses of climate, land-use change, and biomass dynamics using satellite and airborne data.
• Develop and document algorithms and reproducible code for data processing and science products.
• Prepare peer‑reviewed publications and present results to interdisciplinary stakeholders.

Skills

Geospatial analysis

Machine learning

Python

Statistics

Education

PhD in remote sensing / CS / environmental science / statistics

Tools

SAR

LiDAR

Job description

NASA Jet Propulsion Laboratory (JPL) in Pasadena, CA, invites applicants for a senior research role focusing on machine learning and geospatial analysis of forest biomass change. The successful candidate will develop algorithms, work with SAR/LiDAR data, and lead publications in collaboration with interdisciplinary teams.

The role requires a PhD or near completion and strong programming skills in Python, MATLAB, or R, with a track record of quantitative research and peer‑reviewed work.