Postdoctoral Position on Machine Learning in Earth and Environmental Sciences
Geospatial Sensing and Sampling of the Environment
We are a growing and diverse group of active researchers, faculty, and students developing science and technology for intelligent environmental sensing and sampling including in-situ and remotely sensed measurements complemented by physics- and machine learning-based modeling.
Our research efforts focus on advancing the study of terrestrial systems to understand processes ruling landscapes and their change. Our study topics span from atmospheric science, hydrometeorology, hydro climatology, ecohydrology, surface hydrology, hydro informatics, computational fluid dynamics, river science, geomorphology and land surface processes, and planetary science.
We use tools that combine direct field observations from the atmosphere, the ground surface, and underwater environments from intelligent samplers, with remotely sensed information from satellites, radars, radiometers, and sonars onboard of satellites or unmanned aerial or nautical systems and high-performance, distributed hydrological and river computational modeling to develop platforms for decision making under uncertainty. Please look at our research section for a more detailed description of current projects, publications, group members, resources, news, and careers.
Hydrological Sciences (HS)
The Department of Earth, Environmental and Resource Sciences at the University of Texas at El Paso (UTEP), invites applications for a postdoctoral position in Machine Learning applications to Computation Fluid Dynamics and Hydrologic Modeling.
We seek a Ph.D. in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Earth Science, Environmental Science, Environmental Engineering, Civil Engineering, Geography, Water Resources Engineering, Fluvial Geomorphology, or a related physical science field. The appointment is for 1-year initially but is potentially renewable. The Postdoctoral Research Assistant Associate will work under the supervision of Professors Laura V. Alvarez and Hernan A. Moreno within the newly created center for Geospatial Sensing and Sampling of the Environment (GeoSenSE). The guiding research topic is unsupervised and reinforced learning to improve Computational Fluid Dynamics (CFD) models that study macro-turbulence, sediment transport, and bed evolution in large-scale river systems. The desired skills of a potential candidate are: (1) Expertise in both machine and deep learning techniques, and (2) Computer programming using Linux platforms (e.g.++, R, or Python).