7 machine-learning Postdoctoral positions at National Aeronautics and Space Administration (NASA)

Physics Informed Machine Learning for Space Power System Diagnostics and Control

National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 5 hours ago

design. These unanticipated faults and disturbances present great challenges in the detection, isolation, and control of dynamic systems such as electrical power. Recent advances in machine learning

Pre-Fire Burnable Material Assessment and Post-Fire Damage Assessment from Multi-Sensor Remote Sensing

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago

with modern statistical and machine-learning approaches, these data enable robust mapping of fuels, assessment of burn severity, estimation of biomass and structural loss, and tracking of post-fire

A Next-Generation Wildfire Risk Assessment System: Integrating Satellite Data, Deep Learning, and Advanced Statistical Analytics

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago

Earth observation (EO) data from NASA with state-of-the-art machine learning, we can produce a more accurate, dynamic, and actionable measure of wildfire risk. Project Goals and Objectives The primary

Astrodynamics Applications of Artificial Intelligence

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago

on the application of artificial intelligence and machine learning to solving complex trajectory design problems.  Specific applications will focus on tour design strategies and trajectory design within other multi

Earth Science: Impacts

National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 1 month ago

, machine learning and statistical methods to elevate impacts research. There is also opportunity to work at the nexus of water and agriculture, as well as in risk management for suburban landscapes. Location

Quantifying Arctic Ocean Tipping Points

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 month ago

://doi.org/10.3389/fenvs.2025.1473890 Madani, N., et al., & Miller, C. E. (2024). A machine learning approach to produce a continuous solar-induced chlorophyll fluorescence dataset for understanding Arctic

Data Science for Satellite Remote Sensing and Earth Science Research

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 1 month ago

. Description: The post-doctoral fellow will apply machine learning and/or other data science techniques to conduct scientific research, improve remote sensing data products and analysis, integrate machine