ARGONNE

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

Lemont, IL USA · Contract

Sponsorship not specified$73k-$121kDetected 30 days ago

PythonMachine LearningData AnalysisLLMsStatisticsForecastingSupply ChainLogisticsResearchCommunication

About the role

Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The 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.
Argonne encourages everyone to apply for employment.

Develop, improve, and apply computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis.
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.
Collaborate effectively with interdisciplinary teams across Argonne and with external partners.
Experience developing and applying optimization models, such as linear programming, mixed-integer programming, nonlinear optimization, stochastic optimization, robust optimization, or multi-objective optimization.
Experience working with large, heterogeneous datasets and developing reproducible analytical workflows, including using LLMs for the same.

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.
Proficiency in Python/Julia/R and scientific computing/data analysis tools and related libraries.
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.
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.
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.

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.
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.
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.
Postdoctoral Appointee

Apply artificial intelligence, machine learning, LLMs, and advanced statistical techniques to industrial energy systems, manufacturing systems, and commodity supply chains.
Demonstrated expertise in AI, machine learning, statistical modeling, or advanced analytics applied to complex industrial, energy, logistics, manufacturing, or supply chain systems.
Experience integrating machine learning or data-driven methods with optimization and decision-support models.

Publish research in peer-reviewed journals, contribute to sponsor reports and technical deliverables, and present work to collaborators and stakeholders

This listing is sourced directly from ARGONNE's careers page and normalized into a canonical job model.