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.
The successful candidate will contribute to Argonne’s industrial capacity planning, logistics optimization, and supply chain analysis models and apply these tools to support high-impact research on resilient, competitive, and energy-efficient U.S. manufacturing systems. The appointee will be expected to lead core model development and as needed, help expand capabilities in co-optimization of industrial end-use and energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization.
This position offers the opportunity to work with a multidisciplinary team of computational scientists, economists, engineers, and other researchers to develop data-driven, decision-relevant analytical tools for complex industrial systems.
Key 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.
Position 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.
Demonstrated 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.
This position requires an on-site presence at the Argonne campus in Lemont, Illinois.
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.
Preferred Qualifications:
Experience applying AI/ML or advanced analytics to industrial energy systems, manufacturing systems, material flow analysis, or commodity supply chain analysis.
Experience with supply chain network modeling, logistics analysis, infrastructure systems analysis, or resilience and disruption modeling.
Familiarity with hybrid mechanistic-data-driven modeling, surrogate-assisted optimization, or digital twin methods.
Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems.
Experience with geospatial analysis, spatial data integration, or network-based modeling of infrastructure or industrial systems.
Familiarity with high-performance computing, cloud computing, or parallel computing environments for training models and solving optimization problems.
Experience developing dashboards, visual analytics tools, or decision-support interfaces.
Strong record of peer-reviewed publications and demonstrated ability to lead technical research tasks.
Interest in applied research that informs industrial competitiveness, energy systems, manufacturing policy, and supply chain resilience.
This position description documents the general nature and level of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required of job incumbent. Consequently, job incumbent may be required to perform other duties as assigned.
Job Family
Postdoctoral
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full timeThe expected hiring range for this position is $72,879.00-$121,465.00.
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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