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, optimizing logistics in system restoration, 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; he/she will draft research proposals and apply funding from federal agencies (e.g., the Department of Energy and National Science Foundation).

Position Requirements

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

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

• Solid 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.

Preferred Qualifications:

• Knowledge/experience in machine learning.

Job Family

Job Profile

Worker Type

Time Type

As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.  

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis.  Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements.  Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

Please note that all Argonne employees are required to be vaccinated against COVID-19. All successful applicants will be required to provide their COVID-19 vaccination verification as a condition of employment, subject to limited legally recognized exemptions to COVID-19 vaccination.