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National Laboratory of the Rockies

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Postdoctoral Researcher in Uncertainty Quantification and Prediction Reliability 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 AD&D insurance; paid time off and sick leave; paid holidays; and possible relocation reimbursement, subject to eligibility. Employees may also be eligible for performance-, merit-, and achievement-based monetary awards.

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Country

United States

University

National Laboratory of the Rockies

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Keywords

Computer Science
Mathematics
Statistical Inference
Computational Science
Uncertainty Analysis
Probabilistic Modeling
Model Validation
Statistics
Statistical Modelling

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About this position

The National Laboratory of the Rockies is seeking a postdoctoral researcher for a cross-disciplinary project on uncertainty quantification and prediction reliability. The work is based in the Artificial Learning and Intelligent Systems group within NLR’s Computational Science Center in Golden, Colorado.

The project addresses the challenge of making reliable predictions across multiple scales, from laboratory-scale to pilot-scale applications relevant to critical minerals and energy innovation. A central concern is that models trained at one scale may become systematically unreliable at another because relationships change, important mechanisms are missing, or available evidence does not sufficiently constrain predictions.

The researcher will develop original methods to quantify uncertainty in cross-scale prediction models and determine when a model is operating beyond the conditions where its predictions can be trusted. Research themes include sparse-data uncertainty, extrapolation, model-structure uncertainty, model discrepancy, distribution shift, out-of-distribution detection, and distributionally robust prediction.

The role is suited to researchers working in applied statistics, uncertainty quantification for scientific computing, statistical learning theory, reliable or trustworthy machine learning, model validation, and discrepancy modeling. The researcher will work in multidisciplinary teams and collaborate through a mission-driven environment supported by advanced facilities and partnerships with industry, academia, 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 AD&D insurance; paid time off and sick leave; paid holidays; and possible relocation reimbursement, subject to eligibility. Employees may also be eligible for performance-, merit-, and achievement-based monetary awards.

What's required

Applicants must be recent PhD graduates within the last three years and meet the educational requirements before employment begins. Required qualifications include strong experience in statistical modeling, statistical inference, Bayesian methods, probabilistic modeling, uncertainty propagation, aleatoric and epistemic uncertainty, model-form or structural uncertainty, prediction reliability assessment with limited validation data, distribution shift and out-of-distribution detection, model discrepancy, extrapolation assessment, and collaborative software development. Excellent communication skills, multidisciplinary teamwork experience, and programming experience in Python, Julia, or R with version control are required. Self-motivation, creativity, independence, openness, and eagerness to learn are preferred.

How to apply

Apply through the NLR Workday application portal for requisition R14603. Submit a completed application form, cover letter, and resume; applications are not retained for other positions.

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