Sort by
Refine Your Search
-
Country
-
Employer
- Oak Ridge National Laboratory
- National Aeronautics and Space Administration (NASA)
- University of California
- Delft University of Technology (TU Delft)
- EPFL
- North China University of Water Resources and Electric Power
- Texas A&M AgriLife Extension
- University of Liverpool
- University of Washington
- Virginia Tech
- Zintellect
- 1 more »
- « less
-
Field
-
are particularly relevant. The developed numerical models will leverage a large suite of in-situ and remotely sensed observational datasets, spanning from subsurface to atmosphere. You will have experience
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 17 days ago
opportunity? Please email [email protected] Qualifications Candidates should hold a Ph.D. (or complete one before the start date) in hydrology, civil or environmental engineering, remote sensing, geophysics
-
methods. Expertise in coastal landscape ecology, hydrology, and/or geomorphology. Experience analyzing large, heterogeneous environmental, geospatial, remote-sensing, or time-series datasets. Contact
-
, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Develop and apply AI/ML methods to integrate heterogeneous geospatial, remote-sensing, hydrological
-
analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a
-
, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
-
, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
-
and soil assessment, hydrologic monitoring, remote sensing, GIS applications, and processed-based model calibration. The program is designed to develop you into an independent researcher capable
-
consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ and remote sensing data from
-
with remote sensing data application at the time of appointment. Peer-reviewed publications in hydrology, water resources, environmental modeling, remote sensing, or related domains. Strong knowledge