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 candidate will conduct comprehensive supply chain mapping, modeling, and analysis-integrating diverse data sets, applying advanced analytics, and leveraging ML/AI techniques to detect, quantify, and forecast global risks affecting sourcing strategies. It will also include assessing AI-driven demand growth, electricity usage, and their implications for U.S. supply chains and energy infrastructure plans .
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, NumPy, scikit-learn, TensorFlow, 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.
Job Family
Postdoctoral
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
The expected hiring range for this position is $70,758.00-$117,925.00.
Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any 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.
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