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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 Artificial Learning and Intelligent Systems group in NLR’s Computational Science Center has an immediate opening for a Postdoctoral Researcher to pursue a cross-disciplinary research project in uncertainty quantification, with particular emphasis on evaluating prediction reliability across scales (lab-scale to pilot-scale) in applications relevant to the DOE Office of Critical Minerals and Energy Innovation. We are looking for an individual who can reason about uncertainty arising from sparse data, extrapolation, and potentially incorrect model structure.
The successful applicant will develop methods to quantify uncertainty in cross-scale prediction models and identify when a model is operating beyond the conditions where its predictions are reliable. The deeper problem is that a model trained at one scale may be systematically wrong at another because the underlying relationships have changed, important mechanisms are missing, or the available evidence does not adequately constrain the prediction. The successful applicant will conduct original methods research to address this challenge. We are looking for individuals with background in applied statistics, uncertainty quantification for scientific computing, statistical learning theory, reliable or trustworthy machine learning, out-of-distribution detection and distributionally robust methods, and/or model validation and discrepancy modeling.
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* Must meet educational requirements prior to employment start date.
· Self-motivated, creative, and independent thinker
· Open-minded and eager to learn about new application challenges
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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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