10 numerical-methods Postdoctoral positions at National Aeronautics and Space Administration (NASA)

Advancing Impact Monitoring in the Era of LSST, NEO Surveyor, and ADES

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

and accurate orbit determination and hazard assessment methods. Research will center on: (1) developing new observation weighting schemes that exploit the expanded metadata available in the Astrometry

Computational design for spacecraft applications

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

PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously

Computational design for spacecraft applications

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

PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously

Advancing Impact Monitoring in the Era of LSST, NEO Surveyor, and ADES

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

and accurate orbit determination and hazard assessment methods. Research will center on: (1) developing new observation weighting schemes that exploit the expanded metadata available in the Astrometry

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 hours ago

wildland-urban interfaces— across a wide range of climate conditions. Using machine learning methods, we will optimize the weightings of each contributing factor and identify the key drivers of wildfire risk

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

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

wildland-urban interfaces— across a wide range of climate conditions. Using machine learning methods, we will optimize the weightings of each contributing factor and identify the key drivers of wildfire risk

NASA Foundation Models for Earth Science (Ecosystems, Biodiversity, Environmental Variable Downscaling)

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

development of advanced statistical methods (e.g., AI/ML) in recent years. These foundation models represent significant advances in the field of modeling and computer science. The challenge today is to explore

Remote Sensing from Space: NASA Biodiversity and Ecological Conservation Opportunity with the NASA Earth eXchange (NEX)

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

from multiple methods and developing cross-sensor capabilities (e.g., combining SAR and hyperspectral) will be essential for deriving meaningful insights for biodiversity and ecological conservation

NASA Foundation Models for Earth Science (Ecosystems, Biodiversity, Environmental Variable Downscaling)

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

development of advanced statistical methods (e.g., AI/ML) in recent years. These foundation models represent significant advances in the field of modeling and computer science. The challenge today is to explore

Remote Sensing from Space: NASA Biodiversity and Ecological Conservation Opportunity with the NASA Earth eXchange (NEX)

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

from multiple methods and developing cross-sensor capabilities (e.g., combining SAR and hyperspectral) will be essential for deriving meaningful insights for biodiversity and ecological conservation