The Earth Systems Predictability and Resiliency (ESPR) Group provides operational intelligence and system performance forecasting to enable decision support and conducts fundamental and applied research on advancing techniques towards this aim. We use this information to understand and predict how the coupling of human–Earth processes (e.g., climate, water, land use, and energy system interactions) controls complex energy and environmental systems behavior. Our researchers use integrated approaches that include sensing and measurement, environmental forensics, high-performance computing, spatial and non?spatial statistics, spatiotemporal analysis models, coupled human-natural systems models, GIS algorithms, machine learning and artificial intelligence methods, and geovisualization. This includes application areas of climate modeling, extreme events, human-earth interactions and resilience, renewable generation, power system operation and planning, grid resiliency, and smart cities.
The incumbent will have broad knowledge of water resources in relation to impacts of drought on agriculture, energy infrastructure, and urban water supply. Skills in water resources systems modeling are desirable, including analysis of conjunctive use surface and groundwater supply and computation and visualization of associated performance metrics based on water demand shortfalls. Knowledge of high-resolution hydrological models and their applications in drought impacts research would be advantageous.
Preferred candidates will submit cover letters along with a CV that make clear connections to these topics:
Work within multidisciplinary teams representing geospatial/earth science domain knowledge, with active contributions to provide technical insights and pathways to implement solutions.
Contribute research findings to peer-reviewed publications and conference presentations
Develop research plans and lead on project tasks
Support and/or lead new project/sponsor proposals
- BS/BA with 2 years of experienceMS/MA with 0-2 years of experiencePhD with 0 years of experience
Ph.D. in Earth system science, hydrology, geography, physics, engineering, or related field with strong background mathematics, statistics, or uncertainty quantification
Experience in development and execution of physics-based or data-driven models, including demonstrated knowledge of one or more programming languages
Demonstrated experience using machine/deep learning frameworks such as PyTorch, TensorFlow, Keras, MXNet, etc
Demonstrated knowledge of surface hydrology models and water management optimization
Record of peer-reviewed publications
Hazardous Working Conditions/Environment
No hazardous working conditions / environment are anticipated for this position.
Testing Designated Position
This is not a Testing Designated Position (TDP)
Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!
At PNNL, you will find an exciting research environment and excellent benefits including health insurance, flexible work schedules and telework options. PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab’s campus is only a 45-minute flight (or 3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.
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