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..Research and Technical Development - 35%Develop, test, and deploy AI/ML models for:Flood forecasting (nowcasting and short-term prediction)Rainfall estimation and downscalingRemote-sensing fusion (e.g. radar, satellite, SWOT, SMAP, GOES, etc.)Hydrodynamic model optimization and surrogate modelingInfrastructure risk assessment and resilience analyticsIntegrate AI methods with physics-based hydrologic and hydraulic models (e.g., WRF-Hydro, HEC-RAS, OpenFOAM, GSSHA, Delft3D, EFDC, etc.).Data Engineering and 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 and 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 and 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..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..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 managementStrong written and verbal communication skillsStrong project and time management skillsStrong conceptual, organizational, and interpersonal communication skills.Problem-solving and decision-making skills.Ability to acquire accurate and reproducible data
