EPA Postdoctoral Fellowship: Advancing Software and Modeling Tools for Smart Stormwater and Sewer Systems for Small to Medium-Sized Communities
United States Environmental Protection Agency's Office of Research and Development | Posted Oct 24, 2024
Deadline: Feb 14, 2025
- Contact:
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- Experience Level:
- Entry Level
- Job Category:
- Post-doc
- Link to Apply:
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The nation’s aging wastewater and stormwater infrastructure systems are increasingly facing many challenges including an uncertain climate future, affordability crisis, and emerging contaminants especially in smaller communities. These issues are stressing collection and conveyance systems producing more frequent system failures, including overflows, and are placing technical challenges and additional public health and quality of life burdens on these often-disadvantaged communities. The resiliency of these systems, and the ability to effectively monitor and manage them during and after significant wet weather events, can be enhanced.
Decades ago, EPA developed the Storm Water Management Model (SWMM) and continues to upgrade and support the software. SWMM models can be used for engineering planning and design and, along with relatively inexpensive remote sensing/transmitting devices, are increasingly being used to establish “digital twins” of existing systems to allow for improved monitoring, operations, and maintenance towards more resilient collection systems.
Under the guidance of a mentor, research participant activities may include:
* Advancing SWMM related tools to lower the barrier to developing digital twins of collection systems.
* Identifying critical publicly and freely available remotely sensed or model derived datasets that can be assimilated to improve predictive accuracy of digital twins of collection systems.
* Investigating novel real time control algorithms for developing decision support systems for collection system operators.
* Exploring novel approaches to combine traditional physics/process-based hydrological and hydraulic models and artificial intelligence/machine learning approaches for improved computational performance and accuracy.
* Presenting research at professional conferences.
* Publishing research results in peer-reviewed journals.
* Traveling to professional conferences, research facilities, and field sites.