Postdoctoral Appointee – Energy Economist
| Company | Argonne National Laboratory |
|---|---|
| Location | Woodridge, IL, USA |
| Salary | $70758 – $110379.55 |
| Type | Full-Time |
| Degrees | PhD |
| Experience Level | Mid Level, Senior |
Requirements
- Formal education in economics, operations research, public policy, environmental 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
- 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
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
- Develop and apply analytical models and datasets 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
Preferred Qualifications
- Background in economic theories and their application to overall or specific 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, input-output models, and cost modeling
- Familiarity with techno-economic analysis
- Experience with scientific programming languages (e.g., R, Python, Java) and statistical software (e.g., Stata)
- Ability to create visualizations to effectively communicate analysis results