Postdoctoral Appointee: (Power System Modeling and Transient Study) (413876)
Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) works on innovative research to enhance the resilience, efficiency, and sustainability of power grid. Advanced modeling and computation technologies are critical for building the future power grid with sufficient robustness and resiliency. CEEESA is seeking talented and motivated researchers to enhance its capability in solving energy challenges using innovative numerical methods.
The postdoc researcher will work with a team of researchers on developing advanced (dynamic and static) models for power system analyses. The postdoc researcher will perform theoretical study and algorithm development, especially in DOE-sponsored projects. The candidate is expected to authorize peer-reviewed journal/conference publications, develop open-source tools, and help disseminate research results to academic and industry communities. The successful candidate will draft research proposals and apply for funding from federal agencies and private industry.
A Ph.D. in Electrical Engineering, Mechanical Engineering, Applied Mathematics, or other relevant domains.
The candidate is expected to have a basic understanding of power system operations.
Knowledge and independent research capability in dynamic systems, control, and computational algorithms.
Proficient in implementing the algorithms and methods with mainstream programming languages such as Julia, Python, Java, C/C++, etc.
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 power system dynamic modeling and transient analysis, a track record of publications in IEEE Transaction journals, and a highly skilled implementation capability.
Knowledge and independent research capability in power system dynamic model and simulation, especially inverter-based resource model, with track records of publications.
Proficiency in writing scientific research articles and presenting results at academic conferences.
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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