Next-generation wildfire risk assessment using deep learning based high resolution fuel maps an[...]

ORAU; Pasadena (CA); On-site; USD 60,000 - 80,000; Full time

Job summary

A leading aerospace research program in Pasadena, California, is seeking postdoctoral candidates for unique research opportunities focused on wildfire risk assessment using advanced deep learning models and Earth Observation data. Applicants must possess a Doctoral Degree alongside strong expertise in data science, machine learning, and remote sensing. This position offers a chance to contribute significantly to ongoing projects that enhance NASA’s mission in Earth Science.

Qualifications

• Strong background in data science, machine learning, remote sensing, wildfire science, or a related field.
• Experience with deep learning frameworks and satellite observations.
• Strong programming skills and quantitative reasoning.

Responsibilities

• Design and train advanced deep learning models for wildfire assessment.
• Generate high-resolution fuel maps and apply explainable AI techniques.
• Develop integrated wildfire risk metrics through visualization platforms.

Skills

Data science

Machine learning

Remote sensing

Wildfire science

Deep learning frameworks

Geospatial analysis

Education

Doctoral Degree

Tools

Satellite observations

Fire behavior modeling

Job description

Organization

National Aeronautics and Space Administration (NASA)

Application Deadline

11/1/2026 6:00:59 PM Eastern Time Zone

Overview

The NASA Postdoctoral Program (NPP) offers unique research opportunities to highly-talented scientists to engage in ongoing NASA research projects at a NASA Center, NASA Headquarters, or at a NASA-affiliated research institute. These fellowships are competitive and designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology.

Project Description

This project aims to develop a next‑generation wildfire risk assessment platform that tightly integrates Earth Observation (EO) data, deep learning, and dynamic fire behavior modeling. The successful candidate will design and train advanced deep learning models (e.g., U‑Net, Vision Transformers) using multimodal EO datasets (optical, radar, thermal, LiDAR) to generate high‑resolution fuel maps and apply explainable AI techniques to interpret model behavior. These fuel products will be coupled with stochastic fire spread simulators to quantify wildfire behavior (e.g., burn probability, fire intensity) under varying weather scenarios. The project will further explore how these simulation‑derived metrics can enhance EO‑based foundation models for wildfire risk assessment, culminating in a probabilistic, integrated wildfire risk metric delivered through an interactive, stakeholder‑focused visualization platform.

Field of Science

Earth Science

Advisors

• Hugo Lee
• Madeleine Pascolini‑Campbell
• Olga Kalashnikova

Eligibility

Applications with citizens from Designated Countries will not be accepted at this time, unless they are Legal Permanent Residents of the United States. Eligibility is currently open to:

• U.S. Citizens
• U.S. Lawful Permanent Residents (LPR)
• Foreign Nationals eligible for an Exchange Visitor J‑1 visa status
• Applicants for LPR, asylees, or refugees in the U.S. at the time of application with a valid EAD card and I‑485 or I‑589 forms in pending status

Contact

Questions about this opportunity? Email: [e-mail]

Qualifications

Applicants should have a strong background in data science, machine learning, remote sensing, wildfire science, or a related field. Experience with deep learning frameworks, satellite observations (e.g., Sentinel, Landsat, NASA products), and/or geospatial analysis is highly desirable. Familiarity with fire behavior modeling, uncertainty quantification, or explainable AI methods is a plus. Candidates must demonstrate strong programming skills, quantitative reasoning, and the ability to work independently in a collaborative, interdisciplinary research environment.

Eligibility Requirements

• Degree: Doctoral Degree