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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 AI, Learning, and Intelligent Systems (ALIS) Group in the NLR Computational Science Center (CSC) has an opening for a graduate student researcher in Quantum Reservoir Computing (QRC), with special emphasis on applications to power systems modeling, simulation, and control. The researcher will assist in developing quantum reservoir computing algorithms and work using them to model power systems data from, in particular, NLR’s ARIES platform, to explore the limits of quantum reservoir computing and enable novel modalities to address pressing applied problems.
We are looking for a dynamic, motivated researcher with a strong technical background and an interest in the mission of NREL. The successful candidate will collaborate with NREL staff and researchers to design and implement quantum reservoir computing algorithms for power systems analysis and optimization toward the goal of ascertaining and unlocking the potential of developing quantum algorithms with fundamentally superior capabilities than any possible classical approach.
Responsibilities include:
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* Must meet educational requirements prior to employment start date.
Experience with power systems, the technical challenges we face due to a changing grid, and their modeling and analysis.
Deep knowledge of the theory of reservoir computing.
Understanding of quantum computing at level necessary to distinguish real from imagined potential for quantum advantage.
Experience with classical neural network architecture and training
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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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