Postdoctoral Research Associate – Arid Hydrology & Water Systems

About the role

The Texas A&M AgriLife Research Center in El Paso is seeking a Postdoctoral Research Associate to join the Arid Hydrology and Water Systems group. The position focuses on transboundary water resources modeling for sustainable management and binational cooperation, supporting a NASA‑funded Transboundary Model and Data Homogenization project.

Key responsibilities

• Develop, implement, and validate remote‑sensing‑enabled models for surface‑water and groundwater assessment.
• Integrate NASA Earth observation products (e.g., GRACE/GRACE‑FO, SWOT, MODIS, GPM, Landsat, ECOSTRESS, OpenET) with in‑situ and field‑sensor data.
• Configure, calibrate, and apply hydrologic and groundwater modeling systems such as SWAT+, gwflow/MODFLOW, and ParFlow for the Rio Grande and other arid basins.
• Design AI/ML/DL predictive frameworks to evaluate water availability, drought impacts, groundwater depletion, and salinity risk.
• Lead data collection, QA/QC, harmonization, storage, and processing workflows across heterogeneous binational datasets.
• Collaborate closely with U.S.–Mexico stakeholders, including the International Boundary and Water Commission and the North American Development Bank.

Required profile

• Ph.D. in Hydrology, Water Resources, Environmental Engineering, Remote Sensing, or a closely related field.
• Demonstrated experience in transboundary water modeling and stakeholder engagement.
• Strong publication record in peer‑reviewed journals.

Required skills

• Remote‑sensing products: GRACE/GRACE‑FO, SWOT, MODIS, GPM, Landsat, ECOSTRESS, OpenET.
• Hydrologic modeling tools: SWAT+, gwflow, MODFLOW, ParFlow.
• Programming: Python (preferred) and/or MATLAB.
• Machine‑learning frameworks for predictive modeling (e.g., scikit‑learn, TensorFlow, PyTorch).
• GIS and spatial data analysis.

What we offer

• Opportunity to work on a high‑impact NASA‑funded project.
• Collaboration with leading U.S. and Mexican water agencies.
• Access to state‑of‑the‑art remote‑sensing datasets and computational resources.