[ES_JOBS_NET] NREL Engineer/Analyst/Scientist – Energy Systems Air Quality Impacts and Benefits (Golden, CO / Remote)

[Li, Yun]

The National Renewable Energy Laboratory’s (NREL’s) Strategic Energy Analysis Center (SEAC; www.nrel.gov/analysis<http://www.nrel.gov/analysis>) is seeking a researcher with strong programming experience who can, under supervision and mentorship, independently lead the modeling and analysis of more than one project within a growing portfolio of air quality, public health and environmental justice-related projects.

The researcher will join the Impacts Analysis Group within SEAC, alongside researchers who perform a variety of environmental sustainability assessments of energy technologies. With 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. SEAC researchers employ a wide range of advanced modeling methods and tools to assess the impacts of energy technologies, including air quality, public health and environmental justice models and tools as well as code-based life cycle assessment (LCA) models. Description of capabilities and recent projects can be found here: https://www.nrel.gov/analysis/air-quality.html.

SEAC, in partnership with other Centers at NREL, is being asked to quantify the air quality impacts and benefits of energy technologies in a range of domestic and international contexts. We seek applicants interested in a diverse set of source sectors and project applications, interested in developing new air quality/impact methods as well as developing innovative computation solutions. Air quality model development is not the mission of this group; we mainly enhance and apply existing air quality models.

Several bodies of work exist at NREL with emissions inventory, air quality modeling, health impact analysis and environmental justice foci. One body of work focuses on assessing the impact of power system transformation on air quality, collaborating with NREL’s core capability in power sector modeling from decadal to sub-minute timescales (see, e.g., https://www.nrel.gov/analysis/electric-sector-integration.html). For this work, we work both domestically and internationally. We employ reduced complexity air quality models (like InMAP, as well as its global version, Global InMAP), source-specific models (like R-Line or SCICHEM) and are waiting for the right opportunity to use traditional, state-of-the-science air quality models (like WRF-Chem or CMAQ/CMAQ-ISAM). Our projects examine a range of spatial scales from multi-country regions (like SE Asia), national and metropolitan. A flagship example of this modeling is the air quality, health and environmental justice portions of LA100 (see Chapters 9 and 10 here<https://maps.nrel.gov/la100/la100-study/report> as well as follow-on LA100-Equity Strategies study on truck electrification<https://maps.nrel.gov/la100/equity-strategies/reports/truck-electrification#section-0>). Another emerging body of work focuses on use of hydrogen in combustion systems (e.g., to replace natural gas in combustion turbines for peaking loads). Our international portfolio is also growing (see a fact sheet<https://www.nrel.gov/docs/fy23osti/85554.pdf>). A good example of a completed project<https://www.nrel.gov/docs/fy23osti/83832.pdf> examined the air quality and health implications of greater use of renewable energy in the power sector of Southeast Asia. Finally, we have been characterizing emissions from the supply chain of biofuels for many years (https://www.nrel.gov/analysis/biofuels-emissions.html).

NREL has advanced scientific computing capabilities<https://www.nrel.gov/computational-science/index.html> within its Energy Systems Integration Facility<https://www.nrel.gov/esif/index.html>. Applicants are sought that can bring our emerging air quality modeling capability to the next level with machine learning and advanced computing to answer challenging questions at greater spatial, temporal and global scales.

The ideal candidate will have competency in using a wide range of computer tools, programming languages (e.g., Python, R, Fortran) and shell scripting (bash, cshell), as well as demonstrated experience in atmospheric and air quality modeling. Experience with machine learning and advanced computing (e.g., high-performance computing environment) to answer challenging questions at greater spatial, temporal, and global scales is highly desirable. Also desirable is an interest in adjacent disciplines and fields such as life cycle assessment, greenhouse gas inventories, air pollution measurement, citizen science, atmospheric sciences, etc. Knowledge of at least one energy sector (e.g., power, transportation) will be considered a plus.

The candidate should have strong written communication skills, interest in leading drafting of publications; be highly motivated and self-directed; eager to join a fast-paced collaborative work environment; have an ability to “manage up”; and be available to start ideally before end of Winter (start date will be one important factor in prioritizing candidates).
Applications must be submitted through the website and accompanied by a cover letter: https://nrel.wd5.myworkdayjobs.com/NREL/job/Golden-CO/Engineer-Analyst-Scientist---Energy-Systems-Air-Quality-Impacts-and-Benefits_R11883.



Best regards,
--
Yun Li, Ph.D.
Pronouns: she/her/hers
Researcher - Environmental Engineering| Strategic Energy Analysis Center (SEAC)
National Renewable Energy Laboratory (NREL)
15013 Denver West Parkway | Golden, CO 80401
yun.li at nrel.gov<mailto:yun.li at nrel.gov> | www.nrel.gov<http://www.nrel.gov>

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