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# JPL Postdoc - Remote Sensing Data Analytics for Mapping and Modeling Forest Carbon Change and Vulnerability to Climate

- **Company:** NASA Jet Propulsion Laboratory
- **Location:** La Cañada Flintridge, CA, United States
- **Salary:** $83,820
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Computer Programming, Monitoring of Systems, Inventory Management Software, Python (Programming Language), MATLAB, Machine Learning, Data Logging, Data Processing, Deep Learning, Information Technology, Data Analytics, Lidar
- **Published:** November 12, 2026
- **Apply:** https://citjpl.wd5.myworkdayjobs.com/Jobs/job/JPL-Campus/JPL-Postdoc---Remote-Sensing-Data-Analytics-for-Mapping-and-Modeling-Forest-Carbon-Change-and-Vulnerability-to-Climate_R6283

## About the Role

* PhD completed or near completion in remote sensing, computer science, environmental science/engineering, statistics/mathematics, ecology, geography, or a closely related field, with relevant experience in forest applications.
 * Demonstrated experience with quantitative geospatial and satellite data analysis, including Synthetic Aperture Radar (SAR) and/or LiDAR forest remote-sensing methods.
 * Proficiency in advanced machine learning (including time-series methods and experience with generative/foundation AI models) and data-assimilation or ecosystem-model coupling.
 * Strong programming skills in scientific languages (e.g., Python, MATLAB, or R) and solid statistical background.
 * Track record of quantitative research and peer-reviewed publications, and ability to work effectively in interdisciplinary, collaborative teams.

Preferred qualifications

Prior experience with carbon monitoring systems, forest inventory data, cloud-native geospatial workflows, or large-scale remote-sensing product development. Experience working across tropical ecosystems and with stakeholder-relevant applications (policy, management, MRV).

## Description

Lead independent research within a collaborative team using machine learning and process-based models to generate regional and global products and advance understanding of drivers of carbon stock change. Main areas of research will be selected from the following:

 * Geospatial analysis of climate, disturbance (land use change, logging, mortality), fire fuel loads, and biomass/carbon dynamics across tropical forests
 * Analysis of the accuracy of forest biomass change maps based on satellite, airborne, and forest inventory inputs.
 * Development of advance machine learning models including generative and foundational AI to map time series of biomass change from airborne and satellite data
 * Develop, implement, and document algorithms and reproducible code for data processing, analysis, and science-product generation.
 * Lead preparation of peer-reviewed publications and present results to interdisciplinary teams and stakeholders.