Location: Lemont, IL
Cap-Exempt H-1B Position — No lottery required
The Energy Systems and Infrastructure Assessment (ESIA) division provides the rationale for decision makers to improve energy efficiency. We develop and use analytic tools to help the U.S. achieve 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 well as companies to help move the nation toward an economy based on reliable energy. The successful postdoctoral candidate will join a team of Argonne researchers and work closely with federal agencies and other national laboratories. The successful candidate will support technical and economic analyses of power systems with a particular focus on hydropower systems. The successful candidate will develop and apply methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and financial models. The position will include the analysis of hydropower operation and expansion, optimization and equilibrium, market penetration, and interdependencies. This description documents the general nature of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required. - Develop and implement power system modeling tools, with emphasis on simulation and optimization of hydropower systems and their integration with broader electricity markets. - Conduct research on electricity system operations, planning, and market design, including evaluation of economic and environmental trade-offs. - Contribute to projects involving capacity expansion, production cost modeling, and equilibrium modeling of power systems. - Design and apply mathematical optimization models, including linear, mixed-integer, and stochastic programming. - Work with programming languages such as Python, Julia, or C++ to build robust analytical to