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supports the research on energy security and resilience of the power grid at the Energy Department (DOE) through advancement of modeling, simulation, and real-time monitoring of the power grid, including
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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research on cyberphysical electric grid security, data-driven and AI-based power/energy management of electric grid, and electric grid resilience in Grid Resiliency Lab Job Related Minimum Required Education
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motivated Postdoctoral Researcher to join our Grid Planning and Analysis Center (GPAC). This position will focus on tackling critical optimization challenges in medium-term power system planning like
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The Center for Energy, Environmental, and Economic Systems Assessment (CEEESA) works on innovative research to enhance the resilience, efficiency, and affordability of power grids. Advanced
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energy goals. ESIA also develops, deploy, and advance grid technologies that ensure a robust and secure U.S. grid transmission and distribution system. We collaborate with government agencies as
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other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being. Job Description Grid Automation and
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 5 hours ago
manufacturing initiatives that advance U.S. energy security, grid reliability, and national competitiveness. EDF works closely with private-sector partners, other federal agencies, and key stakeholders to finance
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is to develop computationally efficient reduced-order dynamical systems on graph with modern power grid systems as an application. Education and Experience: Applicants must have recently completed a
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, large-scale computational science, and simulation of networked physical systems Familiarity with techniques for sensitivity analysis and handling high-dimensional problems Experience in power grid