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National Laboratory of the Rockies
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Posted Yesterday
Postdoctoral Researcher in Scientific Machine Learning and Cross-scale Predictive Modeling National Laboratory of the Rockies in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Full-time postdoctoral employment at 40 hours per week with an annual salary range of $76,600–$126,400. Benefits include medical, dental, and vision insurance; short-term disability insurance; pension benefits; a 403(b) employee savings plan with employer match; life and accidental death and dismemberment insurance; paid time off and sick leave; and paid holidays. Some positions may be eligible for relocation expense reimbursement, subject to eligibility and applicable conditions.
Country
United States
University
National Laboratory of the Rockies
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About this position
The National Laboratory of the Rockies is seeking a Postdoctoral Researcher in Scientific Machine Learning and Cross-scale Predictive Modeling for its Artificial Learning and Intelligent Systems group within the Computational Science Center.
The project focuses on cross-scale learning from heterogeneous evidence, spanning lab-scale to pilot-scale systems, with relevance to critical minerals and energy innovation. The researcher will develop methods that combine data from different experimental scales, operating conditions, modalities, sampling rates, resolutions, fidelities, and measurement characteristics. The goal is to predict system behavior at previously unseen physical or operational scales, including settings where data are sparse or unavailable.
The work will address situations in which relationships between inputs and outputs change with scale and where source and target data distributions differ. Research directions may include multimodal learning, surrogate modeling, transfer learning, physics-informed machine learning, statistical learning, and hybrid modeling. The researcher will investigate how mechanistic information, physical constraints, known relationships, and changing system context can be incorporated without requiring complete physics-based models.
The position offers a collaborative, multidisciplinary research environment at a national laboratory focused on energy systems, critical minerals, and secure, reliable, and cost-effective energy technologies. The researcher will work with computational scientists, engineers, experimental researchers, and collaborators from academia, industry, and other national laboratories.
Funding details
Full-time postdoctoral employment at 40 hours per week with an annual salary range of $76,600–$126,400. Benefits include medical, dental, and vision insurance; short-term disability insurance; pension benefits; a 403(b) employee savings plan with employer match; life and accidental death and dismemberment insurance; paid time off and sick leave; and paid holidays. Some positions may be eligible for relocation expense reimbursement, subject to eligibility and applicable conditions.
What's required
Applicants must be recent PhD graduates within the last three years and must meet educational requirements before employment begins. Required qualifications include experience developing machine-learning models for scientific or engineering systems; learning across domains or conditions with differing source and target distributions; combining heterogeneous data modalities and experimental conditions; physics-informed and hybrid modeling; representing and adapting to changes in system context, operating conditions, geometry, or scale; excellent communication skills; multidisciplinary teamwork and collaborative software development; and proficiency in Python and version control. Candidates must be able to obtain and maintain a federal Personal Identity Verification card, pass the required background investigation, satisfy Department of Energy access and work-authorization requirements, and pass a pre-employment drug test.
How to apply
Submit an application through the Workday job portal. Include a cover letter and resume; a separate application is required for each position. Applications are accepted during an anticipated submission window of up to 30 days, which may be extended as needed.
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