About the Position 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:Proficiency in Python, Julia, or R and scientific computing/data analysis tools and related libraries. Time-series analysis Neural networks Forecasting Uncertainty quantification Sensitivity analysis Surrogate modeling Clustering Anomaly detection Probabilistic modeling 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. U.S. citizenship is required. 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. Additional Information 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 the job incumbent. Consequently, the job incumbent may be required to perform other duties as assigned. Job Details Job Family: Postdoctoral Job Profile: Postdoctoral Appointee Worker Type: Long-Term (Fixed Term) Time Type: Full Time Expected Hiring Range: $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. Equal Employment Opportunity As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law. Foreign Government Affiliation Disclosure Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. Background Check and Security Requirements All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment. About Argonne National Laboratory Interested in translating science into innovation? Build your career at Argonne. At Argonne, we view the world from a different perspective. Our scientists and engineers conduct world-class research in clean energy, the environment, technology, national security and more. We’re finding creative ways to prepare the world for a better future. To learn more about Argonne's benefits programs, amenities, and other employee programs, visit the main Careers site. To learn more about the exciting science going on at Argonne, visit the Science and Technology page. To request a reasonable accommodation or for other application support, contact: Email: careers@anl.gov Phone: 630-252-2336 If you apply for this position please say you saw it on Computeroxy

About the Position

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:Proficiency in Python, Julia, or R and scientific computing/data analysis tools and related libraries.
- Time-series analysis
- Neural networks
- Forecasting
- Uncertainty quantification
- Sensitivity analysis
- Surrogate modeling
- Clustering
- Anomaly detection
- Probabilistic modeling
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.
U.S. citizenship is required. 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.

Additional Information

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 the job incumbent. Consequently, the job incumbent may be required to perform other duties as assigned.

Job Details

Job Family: Postdoctoral

Job Profile: Postdoctoral Appointee

Worker Type: Long-Term (Fixed Term)

Time Type: Full Time

Expected Hiring Range: $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.

Equal Employment Opportunity

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation.

Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Foreign Government Affiliation Disclosure

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A.

You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

Background Check and Security Requirements

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis.

Please be advised that Argonne positions require upon hire (or may require in the future) for the individual to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

About Argonne National Laboratory

Interested in translating science into innovation? Build your career at Argonne.

At Argonne, we view the world from a different perspective. Our scientists and engineers conduct world-class research in clean energy, the environment, technology, national security and more. We’re finding creative ways to prepare the world for a better future.

To learn more about Argonne's benefits programs, amenities, and other employee programs, visit the main Careers site.

To learn more about the exciting science going on at Argonne, visit the Science and Technology page.

To request a reasonable accommodation or for other application support, contact: