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 (firstname.lastname@example.org) with further questions about the opportunity.