National Renewable Energy Laboratory Postdoctoral Researcher - Industrial Energy and Modeling in Washington, District Of Columbia
Postdoctoral Researcher - Industrial Energy and Modeling
DC - Washington DC
Postdoc (Fixed Term)
Hours Per Week
The successful applicant will join a team of researchers in NREL’s Strategic Energy Analysis Center (SEAC; www.nrel.gov/analysis ) who analyze industrial and renewable energy systems and inter-sector interactions – primarily between the evolving electricity sector and the industrial sectors. Based on objective, technology-neutral analysis, SEAC aims to increase understanding of energy policies, markets, resources, technologies, and infrastructure to address U.S. economic, security, and environmental priorities.
The successful applicant will work with other researchers to design the data and methodological foundations of new open-source modeling capabilities for the industrial sector. These capabilities will be used to analyze potential opportunities to reduce primary energy use and emissions associated with the industrial sector and its products. As an initial effort, the post-doc will identify opportunities to improve characterization of the industrial sector in energy system models with a focus on addressing the geographic, temporal, and operational heterogeneity within the sector. This will be accomplished by performing novel statistical analysis and machine learning on data in industrial sector databases to develop facility prototype models for key industries. Enabling energy analysis of the industrial sector at these greater levels of resolution is critical not only for understanding the sector itself, but for understanding interactions with energy sources and other end-use sectors. Initially, the post-doc hired in this position The researcher will then expand how these models capture unit-process and technology detail and interact with other economic sectors. A primary focus of the data and model development work will be creating open-source data, models, and analysis, as well as fostering interest in the industrial sector within existing communities of energy system modelers.
A strong applicant will have sufficient scientific/engineering knowledge of the design and operation of industrial processes to ground the primarily analytical work of this position. Experience developing material and energy balances and/or manufacturing process designs is a prerequisite. Experience in statistical analysis and machine learning is a prerequisite. Programming skills related to working with quantitative data analysis such as proficiency in an object-oriented language such as Python is a prerequisite. The candidate should have demonstrated experience functioning independently, and ideally will be able to “manage up” to ensure they have the resources needed to complete the assignments on time. Interdisciplinary curiosity, resourcefulness, internal motivation will enable the candidate to function well in the exciting and busy work environment of the nation’s only national lab focused on energy efficiency and renewable energy technologies. Ability to collaborate with NREL colleagues remotely is required. Ability to publish in high-profile journals is desired, as well as to give excellent oral presentations on progress and results. Creative problem solving, and robust research and analytical skills will also be essential.
Must be a recent PhD in a quantitative field focused on the industrial sector such as industrial engineering, chemical engineering, mechanical engineering, or industrial ecology or related engineering field and have graduated within the past three years.
Cover letter must also be proivded with application.
Additional Required Qualifications
Model development and data analysis experience with Python or similar object-oriented languages and experience in developing energy system models.
Familiarity with energy system models, scenario analysis used to identify potential impacts of the evolution of the energy system, life cycle assessment (LCA) tools and methods, grid capacity expansion and production costs, and other tools used in energy system analysis would be helpful to put the analysis work in context.
The ideal candidate will have the ability to quickly learn other programming languages as needed.The ideal candidate will also have knowledge of alternative sources for thermal energy to be used in industrial processes and in electrochemistry technologies.
The ideal candidate will have experience publishing in peer-reviewed journals and presenting at high-profile conferences.
The applicant should desire to work independently within a team environment
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