Postdoctoral Researcher in Scientific Machine Learning and Cross-scale Predictive Modeling
National Laboratory of the Rockies
Postdoctoral Researcher in Scientific Machine Learning and Cross-scale Predictive Modeling
National Laboratory of the Rockies
Renewable energy research and data services
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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 cross-scale learning from heterogeneous evidence (lab-scale to pilot-scale) relevant to the DOE Office of Critical Minerals and Energy Innovation..
The successful applicant will develop methods that combine evidence across different experimental scales, conditions, and modalities to make predictions at unseen physical or operational scales where data may be sparse or unavailable. In this context, the relationships between inputs and outputs may change with scale, and the available data may come from different data sources with different modalities and amounts and have different physical contexts. We are looking for someone who has developed innovative learning methods, including multi-modal and surrogate modeling approaches, transfer learning techniques, physics-informed ML, and/or statistical learning methods.
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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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Renewable energy research and data services
NREL analyzes and develops renewable energy and energy efficiency technologies and provides energy data and research services to government agencies, private companies, and universities. Its offerings include research outputs, data products, and Web services through a developer network that gives clients access to renewable-energy data and information on alternative fuels. The work is funded mainly by government grants and partnerships, and NREL uses its findings to help clients create sustainable energy solutions and policy guidance. The organization differs from many peers by being a government-supported national lab with large, open data resources and a focus on practical deployment, standards, and collaboration across public and private sectors. Its goals include accelerating the adoption of clean energy, advancing energy efficiency, reducing carbon emissions, promoting circular economy practices and decarbonizing its own operations to reach net-zero emissions by 2050.
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