Machine Learning Post Masters Fellow - Hydrology

Please see (and apply at) the full job ad: https://lanl.jobs/search/jobdetails/machine-learning-post-masters-fellow/9703339b-2725-4e3d-b898-84e7aa31d55d

We seek someone interested in developing and applying machine learning techniques to pressing problems related to water security. In particular, your research will include:
 

• Building machine learning models (e.g. LSTMs, Transformers) for streamflow and flood prediction
• Incorporating novel data sources to capture human impacts on watersheds and directly to rivers into your models
• Exploring techniques for building models that incorporate known physical principles
• Developing and deploying methods for understanding what your models learned

This position provides an opportunity to apply your skills toward a variety of impactful problems. Although your project will focus on streamflow prediction, domain-specific knowledge (e.g. hydrology, climate and/or earth sciences) 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 reach out to Jon (jschwenk@lanl.gov) with further questions about the opportunity.