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Golden, Colorado, United States
Onsite
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Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
The National Laboratory of the Rockies (NLR) is seeking a Postdoctoral Researcher to conduct research at the intersection of machine learning, computer vision, large-scale data analytics, and photovoltaic (PV) system performance. The successful candidate will join a multidisciplinary research team working to develop new methods for understanding how the U.S. PV fleet changes over time.
A primary focus of this position will be developing and applying computational methods to identify and characterize changes in utility-scale PV systems using satellite and aerial imagery, large operational datasets, and other geospatial and system-level data. The researcher will develop, adapt, and validate algorithms for image-based identification and change detection and integrate these results with large-scale PV performance datasets.
The successful candidate will work closely with researchers in NLR's PV performance and reliability research community and contribute to the PV Fleets research portfolio. The position requires a researcher who is comfortable developing computational methods, working with large and heterogeneous datasets, evaluating algorithm performance and uncertainty, and translating research methods into reproducible scientific software and analyses. The researcher may also contribute to related projects involving PV fleet evolution, repowering and decommissioning, PV end-of-service analysis, material-flow modeling, and PV circularity.
Specific research activities may include:
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* Must meet educational requirements prior to employment start date.
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The anticipated closing window for application submission is up to 30 days and may be extended as needed.
NLR takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.
* Based on eligibility rules
NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
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All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
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