Postdoctoral Appointee Researcher (Power system dynamics and control)
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, integrating large-scale distributed energy resources, etc. The postdoc researcher will perform theoretical study and algorithm development on optimization methods for solving energy optimization and authorize peer-reviewed journal/conference publications; he/she will develop optimization packages and help disseminate research results to academic and industry community; the successful candidate will draft research proposals and apply funding from federal agencies (e.g., the Department of Energy and National Science Foundation).
A successful candidate will have a solid background in power system dynamics and control, a track records of publications in mathematical optimization journals, a highly skilled implementation capability.
A PhD in Electrical Engineering, Mechanical Engineering, Applied Mathematics, or other relevant domains.
Knowledge and independent research capability in power system dynamic model and simulation, especially inverter-based resource model, with track records of publications.
Knowledge and independent research capability in control and system theories, computational algorithms with track records of publications.
Proficient in implementing control and 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.
Proficiency in implementing machine learning algorithms with mainstream frameworks, such as Tensorflow, Pytorch, Keras.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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