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Argonne National Laboratory · 3 months ago

Postdoctoral Appointee – Supply Chain Analyst

Lemont, IL USA

Full-time

Onsite

New Grad, Entry Level

$71K/yr - $118K/yr

0+ years exp

Argonne National Laboratory is seeking a highly qualified and motivated Postdoctoral Researcher specializing in energy economics and supply chain analysis. The role focuses on evaluating the economic competitiveness of the U.S. in energy-related materials and technologies, conducting supply chain mapping and analysis, and applying advanced analytics and machine learning techniques to inform decision-making for energy deployment and national competitiveness.

EnergySecuritySocial Impact

Culture & Values

No H1B

U.S. Citizen Only

Responsibilities

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

Qualification

Energy economicsSupply chain analysisMachine learningData-driven modelingScientific programmingEconomic analysis techniquesSupply chain mappingCommunicationTeam collaborationIndependent work

Required

Proof of U.S. citizenship, which is required to comply with federal regulations and contract

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

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