Post Doctoral Research Associate

The University of Texas at Arlington Portal • Arlington, TX

18h

About The Position

Water Engineering Research Center and the Department of Civil Engineering in the College of Engineering at the University of Texas at Arlington invites applications for a Postdoctoral Research Associate. The position focuses on applying AI, machine learning, and advanced hydrologic modeling to projects in flood forecasting, remote sensing data assimilation, hydraulic modeling, environmental flows, and decision-support systems. The role involves developing and integrating computational modeling tools to assess future energy-water dynamics and resilience strategies for Texas communities. Ideal candidates should have strong computational and scientific skills and are interested in tackling high-impact, real-world water challenges in collaboration with federal and state agencies, industry partners, and multi-university teams.

Requirements

• Ph.D. in Civil/Environmental Engineering, Hydrology, Hydro informatics, Computer Science, Earth System Science, or related fields.
• Strong background in machine learning, deep learning, or data assimilation.
• Experience with scientific programming (Python, R, MATLAB , etc.) and geospatial tools (ArcGIS, QGIS , GDAL , GeoPandas, etc.).
• Demonstrated publication records in hydrology, AI/ML, or water resources engineering.
• Strong written and verbal communication skills
• Strong project and time management skills
• Strong conceptual, organizational, and interpersonal communication skills.
• Problem-solving and decision-making skills.
• Ability to acquire accurate and reproducible data

Nice To Haves

• Experience with: LLMs, graph neural networks, transformers, or physics-informed neural networks (PINNs).
• Cloud computing ( AWS , Azure, Google Earth Engine).
• HPC workflows ( SLURM , Singularity, Docket).
• Working with large datasets ( NEXRAD , IMERG , ERA5, DHIs, BLE / HEC - RAS models).
• Familiarity with engineering design standards, extreme rainfall analysis, or flood risk management

Responsibilities

• Research & Technical Development – 35%
• Develop, test, and deploy AI/ML models for: Flood forecasting (nowcasting and short-term prediction) Rainfall estimation and downscaling Remote-sensing fusion (e.g. radar, satellite, SWOT , SMAP , GOES , etc.) Hydrodynamic model optimization and surrogate modeling Infrastructure risk assessment and resilience analytics
• Integrate AI methods with physics-based hydrologic and hydraulic models (e.g., WRF -Hydro, HEC - RAS , OpenFOAM, GSSHA , Delft3D, EFDC , etc.).
• Data Engineering & Computational Workflows – 35%
• Curate, preprocess, and analyze large environmental datasets (meteorological, hydrological, hydraulic, traffic, LiDAR, and DEM / BLE datasets).
• Build reproducible pipelines in Python/R/Julia/ SQL for big data geospatial applications.
• Utilize cloud and HPC resources for model training, data assimilation, and simulation.
• Project Leadership & Collaboration – 10%
• Lead technical tasks across multi-institution research teams.
• Prepare technical reports, peer-reviewed journal articles, and conference papers.
• Mentor graduate and undergraduate students.
• Engage with agency stakeholders to translate research into operational tools.
• Proposal Development & Outreach – 20%
• Contribute to competitive grant proposals ( NASA , NSF , NOAA , USACE , TWDB , DOE , etc.).
• Participate in stakeholder workshops, extension/outreach activities, and community engagement related to flood resilience, water management, and environmental flows.

What This Job Offers

• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees