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Join NREL, where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NREL 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 NREL, 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.
NREL’s Energy Systems Optimization and Control team is thrilled to announce an exciting opportunity for a full-time graduate engineering intern with experience in machine learning, time series forecasting, and solar modeling. This is your chance to be at the forefront of energy innovation, working alongside a dynamic, multidisciplinary team of experts from NREL and its collaborators. As a graduate intern, you will dive into a groundbreaking project, developing and implementing cutting-edge AI algorithms for real-time solar forecasting. Your primary mission will be to leverage your expertise in artificial intelligence and statistics to revolutionize energy forecasting. Your deep knowledge of statistical/machine learning, solar forecasting, time series modeling, and inverter analysis will be the driving force behind your success in this role. This full-time position offers the flexibility of optional remote work.
Key Responsibilities:
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* Must meet educational requirements prior to employment start date.
Hands-on experience in energy related time series forecasting, such as participating in energy forecasting competitions
Experience in multi-modal machine learning
Knowledge about PV plants, PV inverters, and PV control
A track record of producing high quality research papers
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The anticipated closing window for application submission is up to 30 days and may be extended as needed.
NREL 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
NREL 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 NREL, 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 NREL 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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