Postdoctoral Appointee: Clean Energy and Advanced Manufacturing Supply Chains and Network Analysis

Argonne National Laboratory

Argonne, IL

Job posting number: #7117249 (Ref:414545)

Posted: December 3, 2022

Application Deadline: Open Until Filled

Job Description

We have an opening for a postdoctoral appointee in the area of supply chain and logistics modeling and analysis, with a focus on clean energy materials and advanced manufacturing technologies.

The appointee will develop models and conduct research and analysis to aid decision-making around topics such as flexible, resilient, sustainable, and secure manufacturing supply chains for materials and products critical to the decarbonization of the U.S. economy. Potential technology areas of interest include energy storage, solar photovoltaics, wind turbines, fuel cells, electrolyzers, semiconductors, transformers and high voltage DC transmission equipment, and plastics. The key research objective will be to understand and inform stakeholders of the economic, environmental, and social justice impacts of optimally designing and deploying materials, manufacturing processes, and associated logistics to meet net-zero-by-2050 goals. The research will build on existing logistics optimization tools and datasets developed by the team and would involve significant collaboration with researchers from other DOE Nationals Laboratories.

The candidate should ideally have a proven record of accomplishment of scholarly work in the application of optimization, as well as other relevant operations research tools such as agent-based modeling, to problems in environmental sustainability. Experience or familiarity with techno-economic analysis and life cycle analysis is desirable. Some analysis work may require development of parametric and physics-based models of key manufacturing processes and technologies. The candidate must demonstrate the ability for convergent thinking and systems analysis that draws from a variety of fields including engineering, economics and sustainability sciences. The candidate will receive a supportive and enabling environment to develop research projects, grow research collaborations, communicate impactful research outcomes in peer-reviewed journals, and support other related projects within the team’s portfolio.

Position Requirements

  • PhD in industrial engineering or any relevant engineering and computational sciences field.

  • Experience with optimization, operations research, and engineering fundamentals.

  • Experience with manufacturing supply chain analysis and optimization.

  • Experience with modeling and simulation for analysis of engineering systems and manufacturing supply chains.

  • Background in optimization, operations research, statistics, and engineering fundamentals.

  • Experience with modern scientific programming languages (e.g., Python, Julia).

  • Ability to integrate diverse knowledge, methods, and perspectives to drive analysis and innovation.

  • Ability to lead research projects, establish collaborations, and work with multidisciplinary research teams.

  • Skilled oral and written communication skills at all levels of the organization.  

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

Preferred Qualifications:

  • Familiarity with techno-economic analysis.

  • Familiarity with developing modeling tools, datasets, or software packages for public use.

  • Ability to develop and synthesize visualizations to effectively communicate analysis results.

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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.



Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.


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