Postdoctoral Appointee – Supply Chain Analyst

Postdoctoral Appointee – Supply Chain Analyst

Join to apply for the Postdoctoral Appointee – Supply Chain Analyst role at Argonne National Laboratory

The Nuclear Technologies and National Security (NTNS) Directorate is seeking a highly qualified and motivated Postdoctoral Researcher specializing in energy economics and supply chain analysis
, with familiarity in machine learning (ML) and artificial intelligence (AI). This role is pivotal in evaluating the economic competitiveness of the U.S. in the production and manufacturing of energy-related materials and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience
.

The successful candidate will apply methods from economics, supply chain risk analysis, and data-driven modeling (including ML/AI where appropriate) to help anticipate vulnerabilities and inform decision-making for energy deployment and national competitiveness.

In this role you will:

• Conduct and contribute to research and model development to enhance the resilience of domestic and global supply chains for clean energy technologies.
• Lead technical and policy analysis to inform decision-makers on manufacturing and energy supply chain strategies.
• Apply advanced analytics and methods to analyze trade, production, and geopolitical data to identify risk in critical supply chains.
• Develop and maintain analytical models, datasets, and risk monitoring tools in collaboration with DOE national laboratories and federal partners.
• Prepare detailed reports and briefings on methodologies, analyses, and findings.
• Collaborate with interdisciplinary teams across DOE National Laboratories.
• Publish impactful research in peer-reviewed journals and support related projects within the team.
• Enhance professional skills, including communication, networking, and leadership.

Position Requirements

• To perform the essential functions of this position successful applicants must provide proof of U.S. citizenship, which is required to comply with federal regulations and contract.
• This level of knowledge is typically achieved through a formal education in economics, operations research, public policy, environmental science, data science, or a related field at the PhD level with zero to five years of employment experience.
• Technical background in economics with a focus on the mineral and energy sectors.
• Proven scholarly work or industry experience in economic and supply chain analysis, computational modeling, or policy analysis.
• Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, Num Py, scikit-learn, Tensor Flow, PyTorch).
• Hands-on experience with data science workflows, including ML/AI model development, training, and evaluation for predictive analytics or decision support.
• Excellent oral and written communication skills in scientific and engineering contexts.
• Ability to integrate diverse knowledge and perspectives to drive innovation.
• Experience working independently and collaboratively in multidisciplinary teams.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Preferred Knowledge, Skills, And Experience

• Background in economic theories and their application to energy, mining, and manufacturing sectors.
• Expertise in metals and materials markets, energy technology manufacturing, or supply chains.
• Proficiency in economic analysis techniques such as econometrics and cost modeling.
• Familiarity with techno-economic analysis and material flow analysis.
• Demonstrated experience in supply chain mapping, risk assessment, and scenario analysis for critical energy and technology sectors.
• Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation systems, and grid reliability.
• Knowledge of how AI-driven energy demand intersects with clean energy deployment, transmission expansion, and supply chain vulnerabilities.
• Ability to design and deploy data pipelines and visualization dashboards to communicate results effectively.
• Familiarity with geospatial data analysis and methods for extracting insights from unstructured data.

Argonne National Laboratory is an equal employment opportunity employer, and we are committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. We encourage everyone to apply for employment. We are committed to nondiscrimination and consider all qualified applicants for employment without regard to any characteristic protected by law.