Overview In this role you apply advanced machine learning to remote sensing data to advance hydrological science and biodiversity assessment within riparian zones. You will collaborate with NASA scientists in an open science environment to develop integrated water cycle modeling tools and synthesize model results. You will lead the preparation of peer reviewed publications and conference presentations, contributing to cross agency scientific leadership. This is a mission driven position at the Hydrological Sciences Lab, offering opportunities to shape modeling capabilities and support biodiversity research at scale.
Responsibilities- Lead analysis and synthesis of model results
- Support and develop peer reviewed publications and conference presentations
- Apply ML methods to remote sensing data for hydrology and ecology applications
- Contribute to development of integrated water cycle modeling tools
- Collaborate with NASA scientists and international partners
Key requirements- Ph.D. in atmospheric science, geophysics, or hydrology
- Geospatial analysis with GIS (QGIS)
- Cloud-based geospatial platforms (Google Earth Engine)
- Experience with remotely sensed and modeled earth science datasets
- Earth systems modeling and forecasting systems, including validation
- Proficiency in Fortran, Python, Java, MATLAB, or R in Unix/Linux environments
- Excellent written and oral communication skills
- Strong organizational skills for workload planning and multitasking
- Team player
- teamwork
- communication skills
- organizational skills
- machine learning
- geospatial analysis
- GIS (QGIS)
