Postdoctoral Appointee: Optimization

Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) works on innovative research to enhance the resilience, efficiency, and sustainability of power grid. Advanced optimization technologies are revolutionizing the way power grid is operated and planned. CEEESA is seeking talented and motivated researchers to enhance its capability in solving energy challenges using optimization technologies.

The postdoc researcher will work with a team of researchers on solving challenging problems using optimization in energy sector, such as optimizing power grid operations, predicting extreme weather hazards and impacts on the power systems, optimizing logistics in system restoration, etc. The postdoc researcher will perform theoretical study and algorithm development on optimization methods for solving energy optimization problems and publish in peer-reviewed journal/conference publications; develop optimization packages and help disseminate research results to academic and industry community; draft research proposals and apply funding from federal agencies (e.g., the Department of Energy and National Science Foundation), and perform other tasks required for this position.

Position Requirements

• A PhD in Electrical Engineering, Industrial Engineering, Operations Research, Applied Mathematics, Computer Science, or other relevant domains.

• Knowledge and independent research capability in optimization theories, computational algorithms with track records of publications.

• Proficient in implementing optimization algorithms with mainstream programming languages such as Julia, Python, Java, C/C++, etc.

• Proficiency in writing scientific research articles and presenting results at academic conferences.

• A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

A successful candidate will have a solid background in optimization theories (mixed-integer programming or nonlinear optimization), a track records of publications in mathematical optimization journals, a highly skilled implementation capability.

Preferred Qualifications:

• Knowledge/experience in machine learning.

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