Jobs:Job-00763: Difference between revisions
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Jonschwenk (talk | contribs) (Created page with "{{CSDMS job details template |JOB title=Machine Learning Post Masters Fellow [Hydrology] |JOB position=Graduate Research Assitant |JOB CSDMS yes no=No |JOB department=Earth and Environmental Sciences |JOB university=Los Alamos National Laboratory (LANL) |State member=New Mexico |MOI meeting country=United States |JOB application_deadline=2022-09-30 |JOB begin review process=Yes |JOB URL application=https://lanl.jobs/search/jobdetails/machine-learning-post-masters-fellow/...") |
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{{CSDMS job details template | {{CSDMS job details template | ||
|JOB title=Machine Learning Post Masters Fellow | |JOB title=Machine Learning Post Masters Fellow - Hydrology | ||
|JOB position=Graduate | |JOB position=Graduate Student | ||
|JOB CSDMS yes no=No | |JOB CSDMS yes no=No | ||
|JOB department=Earth and Environmental Sciences | |JOB department=Earth and Environmental Sciences | ||
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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 | |||
* 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. | 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. | ||
Latest revision as of 06:54, 1 September 2022
Machine Learning Post Masters Fellow - Hydrology
Los Alamos National Laboratory (LANL), New Mexico, United States
Start reviewing process: 30 September 2022
Start reviewing process: 30 September 2022
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Posting:
Position: Graduate Student
Start reviewing process: 30 September 2022
Apply online:
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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.
Of interest for: 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
