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 | |JOB title=Machine Learning Post Masters Fellow - Hydrology | ||
|JOB position=Graduate Student | |JOB position=Graduate Student | ||
|JOB CSDMS yes no=No | |JOB CSDMS yes no=No | ||
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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 | |||
Latest revision as of 06:54, 1 September 2022
Start reviewing process: 30 September 2022
Posting:
Position: Graduate Student
Start reviewing process: 30 September 2022
Apply online:
|
- 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.