Postdoc – Remote Sensing Data Analytics for Mapping and Modeling Forest Carbon Change and Vulnerability to Climate

NASA Jet Propulsion Laboratory

. Lead independent research within a collaborative team using machine learning and process-based models to generate regional and global products .

Posted 11/12/2026full-timePasadena • California • United StatesMid-LevelSenior💰 $83,820 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in advanced machine learning and geospatial analysis, particularly in the context of carbon stock change and forest dynamics. Proficient in developing algorithms and reproducible code for data processing and analysis, with a strong background in interdisciplinary collaboration and peer-reviewed research.

Highest-signal resume keywords

PhD In Remote Sensing, Computer Science, Environmental Science, Statistics, Mathematics, Ecology, GeographyAdvanced Machine Learning ProficiencyQuantitative Geospatial Analysis ExperienceStrong Programming Skills In Python, MATLAB, Or RExperience With Carbon Monitoring Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGeospatial AnalysisData ProcessingStatistical AnalysisTime-Series MethodsGenerative AI ModelsForest Remote-Sensing MethodsData AssimilationEcosystem-Model CouplingQuantitative Research

Soft Skills

Collaborative TeamworkEffective Communication

Tools & Technologies

Synthetic Aperture Radar (SAR)LiDARCloud-Native Geospatial WorkflowsSatellite DataAirborne Data

Industry Keywords

Carbon Stock ChangeTropical EcosystemsForest Inventory DataRemote-Sensing Product DevelopmentPeer-Reviewed Publications

Tech Stack

Tools & technologies

CloudPythonRemote Sensing

About the role

Key responsibilities & impact

Lead independent research within a collaborative team using machine learning and process-based models to generate regional and global products
Advance understanding of drivers of carbon stock change
Conduct geospatial analysis of climate, disturbance, fire fuel loads, and biomass/carbon dynamics across tropical forests
Analyze the accuracy of forest biomass change maps using satellite, airborne, and forest inventory inputs
Develop advanced 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
Present results to interdisciplinary teams and stakeholders

Requirements

What you’ll need

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
Experience with data-assimilation or ecosystem-model coupling
Strong programming skills in scientific languages such as Python, MATLAB, or R
Solid statistical background
Track record of quantitative research and peer-reviewed publications
Ability to work effectively in interdisciplinary, collaborative teams
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)
CV, brief cover letter, publication list, and contact information for three references
Must satisfy JPL/NASA restrictions regarding designated countries, citizenship, legal permanent residency, or other protected status under 8 U.S.C. 1324b(a)(3)

Benefits

Comp & perks

Health plans
Dental plans
Vision plans
Wellbeing plans
Retirement plans
Paid time off
Learning opportunities
Rideshare
Childcare
Flexible schedule
Parental leave
Work-life balance