Jobs:Job-00763: Difference between revisions
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|JOB title=Machine Learning Post Masters Fellow [Hydrology] | |JOB title=Machine Learning Post Masters Fellow [Hydrology] | ||
|JOB position=Graduate Student | |JOB position=Graduate Student | ||
|JOB CSDMS yes no= | |JOB CSDMS yes no=No | ||
|JOB department=Earth and Environmental Sciences | |JOB department=Earth and Environmental Sciences | ||
|JOB university=Los Alamos National Laboratory (LANL) | |JOB university=Los Alamos National Laboratory (LANL) |
Revision as of 17:05, 31 August 2022
Start reviewing process: 30 September 2022
Posting:
Position: Graduate Student
Start reviewing process: 30 September 2022
Apply online:
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- 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.