Postdoctoral Appointee: AI-Driven Industrial Energy Systems and Supply Chain Modeling

About the role

The Industrial Technologies Group within the Energy Systems and Infrastructure Assessment (ESIA) Division at Argonne National Laboratory seeks a highly qualified Postdoctoral Appointee to conduct applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials.

Responsibilities

• Develop, improve, and apply computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis.
• Apply artificial intelligence, machine learning, LLMs, and advanced statistical techniques to industrial energy systems, manufacturing systems, and commodity supply chains.
• Integrate data-driven methods with optimization-based modeling frameworks, including linear, mixed-integer, stochastic, robust, and multi-objective optimization.
• Conduct analyses of industrial system resilience, competitiveness, and operational performance under uncertainty.
• Support model development for co-optimization of industrial end-use systems and energy supply systems.
• Build reproducible computational workflows for data processing, model development, calibration, validation, and scenario analysis.
• Develop visualization and decision-support tools to communicate results to technical and non-technical audiences.
• Publish research in peer-reviewed journals, contribute to sponsor reports and technical deliverables, and present work to collaborators and stakeholders.
• Collaborate effectively with interdisciplinary teams across Argonne and with external partners.

Requirements

• Recent or soon-to-be-completed Ph.D. (typically completed within the last 0-5 years) in computer science, applied mathematics, operations research, engineering, economics, or a related quantitative field.
• Demonstrated expertise in AI, machine learning, statistical modeling, or advanced analytics applied to complex industrial, energy, logistics, manufacturing, or supply chain systems.
• Experience developing and applying optimization models, such as linear programming, mixed-integer programming, nonlinear optimization, stochastic optimization, robust optimization, or multi-objective optimization.
• Experience integrating machine learning or data-driven methods with optimization and decision-support models.
• Background in one or more of the following: time-series analysis, neural networks, forecasting, uncertainty quantification, sensitivity analysis, surrogate modeling, clustering, anomaly detection, or probabilistic modeling.
• Proficiency in Python/Julia/R and scientific computing/data analysis tools and related libraries.
• Experience working with large, heterogeneous datasets and developing reproducible analytical workflows, including using LLMs for the same.
• Software development practices, including documentation and version control.
• Skilled in written and oral communication, with the ability to explain technical methods and findings to multidisciplinary audiences.
• Ability to work both independently and collaboratively in a team-based research environment.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Position Requirements

• US citizenship: 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.