Postdoctoral Appointee Energy Systems Modeling and Life Cycle Analysis for Industrial Decarbonization
- Argonne National Laboratory
- Location: Lemont, USA
- Job Number: 7208418 (Ref #: 417096)
- Posting Date: Jan 18, 2024
Job Description
The Industrial Technologies Team within the Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) is seeking to hire a postdoctoral appointee to conduct modeling-based research on decarbonization of industrial processes and energy systems.
The candidate would conduct research involving the use of in-house energy systems optimization and simulation tools as well as established energy-economy integrated assessment models. In several instances, engineering and/or statistical models would need to be developed to characterize input datasets for energy use, emissions, performance, and cost attributes of incumbent and next-generation industrial technologies. The candidate would also perform life cycle assessment of novel processes and technologies around energy efficiency and process intensification, electrification of unit processes, use of low-carbon fuels and feedstocks, and deployment of carbon capture and storage in industry. The outcomes of the work will inform and advance U.S. clean energy and sustainable manufacturing strategies, investments, and incentives through support from multiple funded U.S. Department of Energy (DOE) projects. The candidate would have the opportunity to collaborate with nationally recognized experts in energy systems analysis and life cycle assessment within Argonne and across different DOE National Laboratories.
Position Requirements
Ph.D. in industrial, mechanical, or chemical engineering, operations research, or related fields.
Demonstrable expertise in systems-level thinking that draws from several scientific disciplines including engineering, economics, and environmental science.
Experience developing mathematical or computational models for simulation and optimization of energy/economic systems in Julia, Python, R, Java, or other scientific programming languages.
Experience working with established integrated assessment models such as TIMES, MESSAGE, TEMOA, GCAM, REMIND, or similar.
Strong command of optimization, statistics, and general engineering fundamentals.
Familiarity with life cycle analysis and techno-economic analysis, particularly in the context of energy and industrial technologies.
Skilled oral and written communication skills as evidenced by a strong record in academic publications and conference presentations.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred Qualifications:
Knowledge of conventional and next-generation energy and manufacturing technologies, and their environmental impacts including energy use, water use, and emissions.
Experience developing software packages, tools, and data sets for public use.
Ability to synthesize effective data visualizations to communicate results from complex data analyses.
Ability to think strategically, use independent judgment, and exercise thought leadership.
Ability to develop evidence-based policy insights in science and technology.
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 FamilyJob Profile
Postdoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
Full timeAs an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
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