Integration of remote sensing data and improved physical process representation in NASA modeling systems

About the role

The Hydrological Sciences Laboratory at NASA's Goddard Space Flight Center seeks a post-doctoral candidate to conduct research on the integration of remote sensing data and models to improve the physical process understanding of hydrometeorological processes. This includes physical processes such as streamflow, evapotranspiration, and interactions with other earth systems such as the atmosphere. Applications to consider extreme events such as floods, droughts, and wildfires are also encouraged.

Responsibilities

• Conduct research on the integration of remote sensing data and models to improve the physical process understanding of hydrometeorological processes.
• Research topics related to remote sensing data, including surface data-assimilation, land surface model and hydrologic model calibration, and use of artificial intelligence to improve existing methods in these areas.
• Research topics related to model development, including improvement of surface and groundwater hydrologic processes and coupling with the atmosphere at regional scales.
• Collaborate with other groups within the Hydrological Sciences Laboratory and other labs within the Goddard Space Flight Center Earth Sciences Division.

Requirements

• Doctoral degree in Earth Science, Hydrology, Meteorology, or a related field.
• Experience with use of modeling systems and processing observation datasets (including remote sensing data).
• Interest in hydrologic processes and their impacts on other parts of the earth system.

Qualifications

• U.S. Citizenship or U.S. Lawful Permanent Resident status.
• Experience with remote sensing data and its integration into physical process models.
• Strong background in hydrologic processes and their impacts on the earth system.
• Ability to work independently and collaboratively with a team.

Skills

• Proficiency in programming languages such as Python, R, or MATLAB.
• Experience with remote sensing data analysis tools such as MODIS, Landsat, etc.
• Knowledge of hydrologic modeling software such as LIS, NU-WRF, and NLC.
• Experience with machine learning and artificial intelligence techniques.

Benefits

• One to three-year fellowship with competitive funding.
• Opportunities to work with leading experts in the field.
• Access to state-of-the-art facilities and equipment.
• Professional development opportunities through workshops and conferences.

Pay

• Salary range based on experience and qualifications.

Schedule

• Full-time position.

Contact

Please email npp@orau.org for questions about this opportunity.