Postdoctoral Researcher – Scientific Machine Learning, Cross-scale Predictive Modeling

Advanced Energy Systems - Colorado School of Mines & NLR

. Pursue a cross-disciplinary research project in cross-scale learning from heterogeneous evidence from lab-scale to pilot-scale .

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 developing machine learning models for scientific applications, particularly in cross-scale predictive modeling and multimodal data integration. Strong collaboration skills are essential for working within multidisciplinary teams and contributing to innovative energy research.

Highest-signal resume keywords

PhD In Relevant FieldMachine Learning Model DevelopmentMultimodal Data IntegrationPhysics-Informed ModelingCollaborative Software Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical Learning MethodsTransfer LearningSurrogate ModelingModel RepresentationHybrid ModelingData Combination TechniquesPredictive ModelingExperimental Scale AnalysisMechanistic Modeling

Soft Skills

Excellent Communication SkillsTeam Collaboration

Tools & Technologies

PythonVersion Control

Certifications & Qualifications

Personal Identity Verification (PIV) Card

Industry Keywords

Cross-Scale LearningEnergy InnovationCritical Minerals ResearchMultidisciplinary ResearchExperimental Conditions

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Pursue a cross-disciplinary research project in cross-scale learning from heterogeneous evidence from lab-scale to pilot-scale
Develop methods combining evidence across experimental scales, conditions, and modalities
Make predictions at unseen physical or operational scales where data may be sparse or unavailable
Develop innovative learning methods, including multimodal and surrogate modeling, transfer learning, physics-informed machine learning, and statistical learning methods
Collaborate with multidisciplinary research teams, industry, academia, and other national laboratories
Contribute to scientific machine learning and cross-scale predictive modeling for energy innovation and critical minerals research

Requirements

What you’ll need

Must be a recent PhD graduate within the last three years
Must meet educational requirements prior to employment start date
Demonstrated experience developing ML models for scientific or engineering systems
Demonstrated experience learning from one set of conditions or domains and predicting in another, including differing source and target distributions
Demonstrated experience combining data with different modalities, sampling rates, resolutions, fidelities, measurement characteristics, costs, accuracies, or experimental conditions
Demonstrated experience in physics-informed and hybrid modeling
Ability to incorporate mechanistic models, physical constraints, or known relationships when useful
Demonstrated experience in model representation and adaptation across system context, operating conditions, geometry, or scale
Excellent communication skills
Demonstrated experience working in multidisciplinary teams and collaborative software development
Demonstrated experience with Python and version control
Ability to obtain and maintain a federal Personal Identity Verification (PIV) card
Favorable background investigation
Must pass a pre-employment drug test

Benefits

Comp & perks

Medical, dental, and vision insurance
Short-term disability insurance
Pension benefits
403(b) Employee Savings Plan with employer match
Life and accidental death and dismemberment (AD&D) insurance
Personal time off (PTO) and sick leave
Paid holidays
Potential eligibility for performance-, merit-, and achievement-based monetary awards
Potential relocation expense reimbursement
Robust professional development opportunities
State-of-the-art facilities and multidisciplinary research teams