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About this JPL Postdoc - Remote Sensing Data Analytics for Mapping and Modeling Forest Carbon Change and Vulnerability to Climate role at NASA Jet Propulsion Laboratory

NASA Jet Propulsion Laboratory · Onsite · JPL Campus

Job Details

New ideas are all around us, but only a few will change the world. That’s our focus at JPL. We ask the biggest questions, then search the universe for answers—literally. We build upon ideas that have guided generations, then share our discoveries to inspire generations to come. Your mission—your opportunity—is to seek out the answers that bring us one step closer. If you’re driven to discover, create, and inspire something that lasts a lifetime and beyond, you’re ready for JPL.

Located in Pasadena, California, JPL has a campus-like environment situated on 177 acres in the foothills of the San Gabriel Mountains and offers a work environment unlike any other: we inspire passion, foster innovation, build collaboration, and reward excellence.

Responsibilities

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.

Desired qualifications

  • 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).

Application

To apply, submit a CV, a brief cover letter describing research interests and which task you wish to lead, a publication list, and contact information for three references. Review of applications will begin immediately and continue until positions are filled.

JPL has a catalog of benefits and perks that span from the traditional to the unique. This includes a variety of health, dental, vision, wellbeing, and retirement plans, paid time off, learning, rideshare, childcare, flexible schedule, parental leave and many more. Our focus is on work-life balance, and living healthy, fulfilling lives as we Dare Mighty Things Together. For benefits eligible positions, benefits are effective the first day of the month coincident with or immediately following the employee’s start date.

For further benefits information click Benefits and Perks

The hiring range displayed below is specifically for those who will work in or reside in the location listed. In extending an offer, Jet Propulsion Laboratory considers factors including, but not limited to, the candidate’s job related skills, experience, knowledge, and relevant education/training.

The typical full time equivalent annual hiring range for this job in Pasadena, California.

$83,820 - $83,820

JPL is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, citizenship, ancestry, age, marital status, physical or mental disability, medical condition, genetic information, pregnancy or perceived pregnancy, gender, gender identity, gender expression, sexual orientation, protected military or veteran status or any other characteristic or condition protected by Federal, state or local law.

In addition, JPL is a VEVRAA Federal Contractor.

EEO is the Law.

EEO is the Law Supplement

Pay Transparency Nondiscrimination Provision

The Jet Propulsion Laboratory is a federal facility. Due to rules imposed by NASA, JPL will not accept applications from citizens of designated countries or those born in a designated country unless they are U.S. Citizens, Legal Permanent Residents of the U.S or have other protected status under 8 U.S.C. 1324b(a)(3). The Designated Countries List is available here.

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About NASA Jet Propulsion Laboratory

Formed in 1936, the NASA Jet Propulsion Laboratory is a federally funded research and development center managed for NASA by the California Institute of Technology (Caltech.) JPL joined NASA as an FFRDC when the agency was founded in 1958. JPL helped open the Space Age by developing America’s first Earth-orbiting science satellite, creating the first successful interplanetary spacecraft, and sending robotic missions to the solar system. Today JPL continues its world-leading innovation, implementing programs in planetary exploration, Earth science, space-based astronomy and technology development while applying its capabilities to technical and scientific problems of national significance. Ab

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