Postdoctoral Researcher, Uncertainty Quantification, Prediction Reliability

Advanced Energy Systems - Colorado School of Mines & NLR

. Pursue a cross-disciplinary research project in uncertainty quantification .

Posted 11/9/2026full-timeGolden • Colorado • United StatesMid-LevelSenior💰 $76,600 - $126,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in uncertainty quantification and statistical modeling, with a strong focus on Bayesian methods and probabilistic modeling. Capable of collaborating in multidisciplinary teams and contributing to software development while effectively communicating complex concepts.

Highest-signal resume keywords

Statistical ModelingBayesian MethodsUncertainty PropagationProgramming in PythonCollaborative Software Development

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Applicant Tracking System Keywords

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Hard Skills

Statistical InferenceProbabilistic ModelingAleatoric UncertaintyEpistemic UncertaintyModel DiscrepancyStructural UncertaintyDistribution ShiftOut-of-Distribution DetectionPrediction ReliabilityModel Evaluation

Soft Skills

Excellent Communication SkillsTeam Collaboration

Tools & Technologies

PythonJuliaRVersion Control Software

Certifications & Qualifications

Personal Identity Verification (PIV) Card

Industry Keywords

Uncertainty QuantificationCross-Scale PredictionModel ReliabilityMultidisciplinary Research

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Pursue a cross-disciplinary research project in uncertainty quantification
Evaluate prediction reliability across scales from lab-scale to pilot-scale
Reason about uncertainty arising from sparse data, extrapolation, and potentially incorrect model structure
Develop methods to quantify uncertainty in cross-scale prediction models
Identify when models operate beyond conditions where predictions are reliable
Conduct original methods research on model reliability across changing scales and conditions
Address effects of changed underlying relationships, missing mechanisms, and insufficient evidence on predictions
Collaborate with multidisciplinary research teams and contribute to collaborative software development

Requirements

What you’ll need

Recent PhD graduate within the last three years
Must meet educational requirements prior to employment start date
Experience with statistical modeling, statistical inference, Bayesian methods, probabilistic modeling, and uncertainty propagation
Experience distinguishing and estimating aleatoric and epistemic uncertainty, and model-form or structural uncertainty
Understanding of methods for assessing whether predicted uncertainty corresponds to actual prediction errors, especially with limited validation data
Understanding of distribution shift and out-of-distribution detection
Experience with model discrepancy and structural uncertainty
Experience evaluating predictions outside the training domain and recognizing unsafe or unsupported extrapolation
Excellent communication skills
Experience working in multidisciplinary teams and collaborative software development
Programming experience in Python, Julia, or R
Experience with version control software
Must be able to obtain and maintain a federal Personal Identity Verification (PIV) card, including a favorable background investigation
Must pass a pre-employment drug test prior to commencing employment

Benefits

Comp & perks

Medical, dental, and vision insurance
Short-term disability insurance (based on eligibility rules)
Pension benefits (based on eligibility rules)
403(b) Employee Savings Plan with employer match (based on eligibility rules)
Life insurance
Accidental death and dismemberment (AD&D) insurance
Personal time off (PTO)
Sick leave
Paid holidays
Performance-, merit-, and achievement-based awards with a monetary component (eligibility not guaranteed)
Relocation expense reimbursement for some positions (eligibility not guaranteed)
Robust professional development opportunities
State-of-the-art facilities and multidisciplinary research teams