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Machine learning hydrology postdoc
Los Alamos National Laboratory (LANL), New Mexico, United States
Start reviewing process: 1 March 2022

We seek someone interested in developing and applying machine learning techniques for impactful climate-related problems. Your research will include building machine learning models (e.g. LSTMs) for streamflow and flood prediction, exploring techniques for translating internal model states to physical concepts, and software development.

This position provides an opportunity to apply your skills toward a variety of impactful problems. Although your primary project will focus on generating flood risk projections under various climate scenarios, domain-specific knowledge (e.g. hydrology) is not required. Depending on your interests and time, you may have opportunities to contribute to other projects as well, including for example modeling mosquito-borne diseases, mapping permafrost presence with ML, or estimating water quality from remotely-sensed images. Our teams will provide context, background, and guidance as you familiarize yourself with the domain-specific applications. While the overall research goals for these projects have been established, there is significant flexibility in the way these goals can be achieved, and novel approaches are encouraged.

Please visit for more information and/or to apply, and email with any questions you have about the position.

Of interest for:
  • Terrestrial Working Group
  • Hydrology Focus Research Group
  • River Network Modeling Initiative