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National Laboratory of the Rockies

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Postdoctoral Researcher in Numerical Optimization and Adaptive Experimental Design for Cross-Scale Energy Applications National Laboratory of the Rockies in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Full-time salary of $76,600–$126,400 per year for a 40-hour work week, determined based on education, training, experience, work quality and other factors. 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, sick leave and paid holidays. Eligible positions may receive performance-, merit- or achievement-based awards and relocation expense reimbursement

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Country

United States

University

National Laboratory of the Rockies

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Keywords

Computer Science
Mathematics
Computational Science
Active Learning
Statistics
Renewable Energy Systems
ML

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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 s...

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Posted Yesterday

About this position

The National Laboratory of the Rockies is seeking a Postdoctoral Researcher in Numerical Optimization and Experimental Design for a cross-disciplinary project in the Artificial Learning and Intelligent Systems group within the Computational Science Center.

The research focuses on optimization and adaptive experimental design for cross-scale scientific applications, ranging from laboratory-scale to pilot-scale systems, with relevance to critical minerals and energy innovation. The postdoctoral researcher will develop optimization and sampling methods that use predictions from models trained on heterogeneous, cross-scale evidence together with quantified uncertainty.

A central goal is to determine which experiments to run, which measurements to collect, and which operating conditions or scales to use in order to obtain the information most valuable for improving model predictions at the target experimental scale. The work combines numerical optimization, optimal experimental design, active learning, Bayesian optimization, sequential decision-making, information theory, adaptive sampling, surrogate-based optimization, and statistical design of experiments.

The researcher will address practical challenges involving noisy, sparse, missing, and messy data, uncertain models, multi-fidelity and multi-modal sampling, experiment cost, feasibility, safety, time, and operational constraints. The project offers an opportunity to create original computational methodologies and collaborate with multidisciplinary scientists, engineers, industry partners, academic researchers, and other national laboratories in a mission-driven energy research environment.

Funding details

Full-time salary of $76,600–$126,400 per year for a 40-hour work week, determined based on education, training, experience, work quality and other factors. 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, sick leave and paid holidays. Eligible positions may receive performance-, merit- or achievement-based awards and relocation expense reimbursement

What's required

Applicants must be recent PhD graduates within the last three years and meet educational requirements before the employment start date. Required qualifications include demonstrated experience in optimal experimental design, active learning, sequential design, Bayesian optimization, multi-fidelity and multi-source sampling, information and decision theory, constrained and cost-aware sampling, scientific computing, optimization, machine learning and AI. Applicants must be able to develop efficient sequential decision algorithms and demonstrate Python and version-control experience, excellent communication skills, and experience working in multidisciplinary teams and collaborative software development. Self-motivation, creativity, independence, open-mindedness, and willingness to learn are preferred. Employment requires the ability to obtain and maintain a federal PIV card, pass a background investigation and, where applicable, a pre-employment drug test; offers are conditional on work authorization and DOE facility access.

How to apply

Apply through the Workday job portal using the application form for this position. Submit a cover letter and resume; applications are not retained for future positions. The anticipated submission window is up to 30 days and may be extended.

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