49 data "https:" "https:" "https:" "https:" "LaTIM Brest" Postdoctoral positions at National Aeronautics and Space Administration (NASA)

A Multi-Modular AI Agent for ISSM Modeling, Data Fusion, and Geophysical Analysis

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

, identify physically consistent constraints, and recommend data assimilation configurations tailored to specific scientific objectives. All AI modules will be designed within a physics-constrained framework

Advanced Aerosol Data Assimilation of the GEO-LEO Constellation

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

(GMAO), is a comprehensive Earth system model and data assimilation system. The GMAO assimilates aerosol optical depth (AOD) observations from space-borne (e.g. MODIS, VIIRS) and ground-based (AERONET

Studying Biosphere Drivers, Change, and Impacts in Terrestrial and Coastal Ecosystems Using Remote Sensing Data and Modeling

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

process models often lack the spatially explicit, high-frequency data needed to accurately simulate these rapidly changing dynamics and their cascading effects on vegetation health, carbon cycling, and

Data Assimilation of Combined Lidar and Polarimeter Observations for the AOS Mission

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

distribution on a global scale. This project will focus on developing a strategy to best utilize this data in a global atmospheric data assimilation framework. Activities that would be involved in this project

Strengthening NASA Air Quality Observations with Low-Cost Sensor Data

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

. Description: NASA Ames Research Center is looking for a postdoctoral assistant with research interests in low-cost sensors and/or data fusion to join our Trace Gas measurement group. This will involve working

Effect of Wildfires on Ocean Biogeochemistry

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

marine planktonic ecosystems. For example, the 2019-2020 Australian wildfires triggered widespread phytoplankton blooms, evidenced in chlorophyll data obtained from satellite remote sensing and in situ

Predicting Precipitation-Driven Disruptions to Critical Infrastructure

National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 23 hours ago

traditional predictive attempts and limits the availability of training data for high-resolution atmospheric and hydrological models. This limitation is compounded by the fact that many atmospheric reanalysis

Advancing Terrestrial Ecology, Wildfire Fuel Monitoring, or Agricultural Applications, or Associated Activities through VSWIR Imaging Spectroscopy

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

invites research proposals that utilize high-fidelity imaging spectroscopy data to address critical questions in terrestrial ecosystem function, biodiversity and conservation, precision agriculture

Determining Main Belt and NEO Origins and Increasing Reliability of Asteroid Detections

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

distribution information, but did not comprehensively de-bias the results. This work will build from the prior studies, based on the full surveyed populations of those missions, more comprehensive knowledge of

Prithvi-EO Foundation Model for Forest Structure, Biomass, and Fire-Relevant Metrics

National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 23 hours ago

carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as