39 big-data-and-machine-learning-phd Postdoctoral positions at National Aeronautics and Space Administration (NASA)

A Next-Generation Wildfire Risk Assessment System: Integrating Satellite Data with Deep Learning Insights

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago

observations and model-based data. Apply machine learning techniques to optimize factor weightings and reveal critical wildfire drivers. Validate models using multi-source wildfire data (MODIS, VIIRS, and NOAA

A Next-Generation Wildfire Risk Assessment System: Integrating Satellite Data with Deep Learning Insights

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 months ago

observations and model-based data. Apply machine learning techniques to optimize factor weightings and reveal critical wildfire drivers. Validate models using multi-source wildfire data (MODIS, VIIRS, and NOAA

Science with Next Generation, Large Radio Arrays

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 3 months ago

or in collaborations, on a variety of telescopes, including the Jansky Very Large Array (JVLA) and Very Long Baseline Array (VLBA), the Long Wavelength Array at Caltech's Owens Valley Radio Observatory

Data Science for Satellite Remote Sensing and Earth Science Research

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 4 hours 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

Ecosystem Structure and Function from the Fusion of Lidar, Hyperspectral, and Thermal Data

National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago

. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). Remote sensing data provide essential information on the spatial and temporal variability in vegetation

Data Science for Satellite Remote Sensing and Earth Science Research

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 2 months 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

Investigating Air Quality Challenges and Impacts in California’s Central Valley

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 4 hours ago

status Questions about this opportunity? Please email npp@orau.org Qualifications PhD in atmospheric science, environmental engineering, public health, data science, or relevant disciplines. Experience

Computational, Analytical and Experimental Thermodynamics to Investigate the Presence, Stability, and Implications of Organic Matter in Ocean Worlds

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 3 months ago

. Description: Ocean worlds such as Europa, Enceladus, Titan and Ceres are exciting, potentially habitable worlds in our own solar system. A large mass fraction of these bodies could be composed of organic matter

High Fidelity Modeling of Heating during Entry to the Outer Planets

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 4 hours ago

parameters such as rate and transport coefficients, state-to-state transition data and radiation transition moments. This post-doctoral position will examine higher fidelity approaches that address thermal and

Remote sensing applications for mapping and monitoring global forest carbon stock and dynamics

National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 months ago

research using various statistical and machine learning approaches to develop regional and global products and improve our understanding of the drivers of carbon stock changes across a variety of ecosystems