Postdoctoral Appointee: Modeling and Analysis of Industrial Decarbonization and Material Circularity Pathways

Argonne National Laboratory - Lemont, IL

Full-time - Entry Level

Lemont, IL

Gasoline Stations and Fuel Dealers

About the position

The Buildings & Industry Group within the Energy Systems and Infrastructure Analysis (ESIA) Division at Argonne is seeking a postdoctoral appointee to conduct research and modeling-based analysis focused on decarbonization pathways for energy- and emissions-intensive industries and material circularity pathways in a low-carbon future. The role involves developing insights critical to strategic technology investments at federal and state levels, utilizing a range of analytical tools and methodologies.

Responsibilities

• Conduct research on decarbonization pathways for energy- and emissions-intensive industries.
• Analyze material circularity pathways in a low-carbon future.
• Develop analysis insights for DOE-sponsored projects.
• Utilize process and thermodynamic modeling, life cycle assessment, techno-economic analysis, and agent-based modeling.
• Build on established modeling tools at Argonne and develop new models/tools as needed.
• Interface with federal and state government sponsors and team members from other national laboratories.

Requirements

• Ph.D. in mechanical, industrial, chemical engineering or related fields.
• Expertise in systems-level thinking across engineering, economics, and environmental science.
• Experience developing mathematical or computational models for energy/economic systems in ASPEN Plus® and/or programming languages like Julia, Python, R, or Java.
• Experience with life cycle assessment tools such as GREET®, OpenLCA, SimaPro®, or GaBi®.
• Strong oral and written communication skills demonstrated through academic publications and presentations.
• Commitment to Argonne's core values of impact, safety, respect, integrity, and teamwork.

Nice-to-haves

• Knowledge of energy and manufacturing technologies and their environmental impacts.
• Coursework or project experience in mathematical optimization, statistics, or machine learning.
• Experience developing software packages and tools for public use.
• Ability to create effective data visualizations for complex data analyses.
• Strategic thinking and independent judgment capabilities.
• Ability to develop evidence-based policy insights in science and technology.

Benefits

• Diverse and inclusive workplace
• Collaborative scientific discovery and innovation
• Equal employment opportunity and affirmative action employer