Postdoctoral Appointee: Clean Energy and Advanced Manufacturing Supply Chains and Network Analysis
- Argonne National Laboratory
- Location: Argonne, IL
- Job Number: 7117249 (Ref #: 414545)
- Posting Date: Dec 3, 2022
- Application Deadline: Open Until Filled
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.
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.
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 FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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