Energy Supply Chain Modeling & Analytics Postdoctoral Research Associate

Energy Supply Chain Modeling & Analytics Postdoctoral Research Associate

Idaho Falls, , United States (On-site)

Idaho National Laboratory (INL) is seeking a well-qualified Postdoctoral Associate to advance decision-relevant supply chain analysis for energy technologies. In this role, you will develop and apply quantitative methods to understand supply chain risk, resilience, and domestic manufacturing opportunities across bioenergy, electric vehicles, batteries, and critical materials.

The ideal candidate blends strong systems thinking with hands‑on technical execution: you can translate a complex, real‑world supply chain question into an analyzable model, build the code and data pipeline to run it at scale, and communicate results that stakeholders can act on.

Our team works a 9x80 schedule located out of our Idaho Falls facility with every other Friday off. You will report to your group lead and department manager.

You will have demonstrated proficiency in one or more of the following: statistics, mathematics, programming (Python and/or Java), systems modeling (e.g., system dynamics, agent‑based modeling, discrete event simulation), geospatial analysis, AI/ML, and other quantitative approaches used in operations research, management science, and supply chain modeling.

Responsibilities Include:

• Work in a multidisciplinary team using a range of modeling tools and approaches to solve problems in supply chain resilience, manufacturing, and energy security across bioenergy, electric vehicles, batteries, and critical materials.
• Design, develop, and apply computational models (e.g., discrete event simulation, system dynamics, agent‑based modeling, network models, optimization/operations research, AI/ML, and geospatial methods) to evaluate risks, constraints, and mitigation strategies in complex supply chains.
• Build reproducible analysis workflows and software components, including data pipelines, model interfaces, and visualization or web‑based tools. Where appropriate, leverage high‑performance computing (HPC) and scalable architectures.
• Engage with internal and external collaborators to scope work, define requirements, and deliver decision‑focused outputs that meet Department of Energy and industry expectations and project milestones.
• Contribute to INL’s technical reputation through major contributions to presentations, reports, peer‑reviewed journal articles, invention disclosures, and other professional activities.
• Operate as a strong individual contributor while also taking direction from teammates and peers, consistently delivering scope within cost and schedule.
• PhD in computer science, systems engineering, operations research, industrial/mechanical engineering, environmental science/engineering, chemical engineering, or a related field.
• PhD requirements must be completed by commencement of appointment and within the past 5 years.
• Proficiency in Python or Java, including experience developing reproducible analysis code, model interfaces, and/or machine learning workflows.
• Demonstrated proficiency in statistics, mathematics, programming, systems modeling, geospatial methods, AI/ML, or another quantitative field directly applicable to operations research, management science, and supply chain modeling.
• Demonstrated oral and written communication skills (in English), including a developing publication record in relevant fields of research.
• Experience in computational analysis and model development using one or more modeling paradigms (simulation, optimization, forecasting, network modeling).

The ideal candidate will possess:

• Demonstrated understanding of supply chain systems and markets, including disruption dynamics, bottlenecks, substitution, logistics constraints, and resilience metrics.
• Experience with analysis software and tools such as discrete event simulation, system dynamics, agent‑based modeling, GIS/geospatial tool chains, statistical analysis, and life cycle analysis tools.
• Experience in techno‑economic analysis and/or life cycle assessment, including uncertainty and sensitivity analysis.
• Experience with proposal development or contributing to new R&D directions (scoping, framing…