Jobs:Job-00763: Difference between revisions
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{{CSDMS job details template | {{CSDMS job details template | ||
|JOB title=Machine Learning Post Masters Fellow [Hydrology] | |JOB title=Machine Learning Post Masters Fellow [Hydrology] | ||
|JOB position=Graduate | |JOB position=Graduate Student | ||
|JOB CSDMS yes no= | |JOB CSDMS yes no=Yes | ||
|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) | ||
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|Working group member=Marine Working Group, Terrestrial Working Group, Coastal Working Group, Hydrology Focus Research Group, Human Dimensions Focus Research Group, Artificial Intelligence & Machine Learning Initiative, River Network Modeling Initiative | |Working group member=Marine Working Group, Terrestrial Working Group, Coastal Working Group, Hydrology Focus Research Group, Human Dimensions Focus Research Group, Artificial Intelligence & Machine Learning Initiative, River Network Modeling Initiative | ||
|JOB bodytext=We seek someone interested in developing and applying machine learning techniques to pressing problems related to water security. In particular, your research will include: | |JOB bodytext=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 | - 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 | - 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 | - Exploring techniques for building models that incorporate known physical principles | ||
- Developing and deploying methods for understanding what your models learned | - Developing and deploying methods for understanding what your models learned | ||
Revision as of 17:02, 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.
Please email Jon (jschwenk@lanl.gov) with questions and see the full job posting here: https://lanl.jobs/search/jobdetails/machine-learning-post-masters-fellow/9703339b-2725-4e3d-b898-84e7aa31d55d