Posting Title

Postdoctoral Researcher in Numerical Optimization and Experimental Design

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Location

CO - Golden

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Position Type

Postdoc (Fixed Term)

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Hours Per Week

40

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Working at NLR

NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.

Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.

At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

Job Description

The Artificial Learning and Intelligent Systems group in NLR’s Computational Science Center has an immediate opening for a Postdoctoral Researcher to pursue a cross-disciplinary research project in optimization and adaptive experimental design for cross-scale scientific applications (lab-scale to pilot-scale) relevant to the DOE Office of Critical Minerals and Energy Innovation.

 

The successful applicant will develop optimization and sampling methods that leverage predictions from models that were trained on cross-scale heterogeneous evidence and their corresponding uncertainties. The sampling strategies must determine which experiments to run, which measurements to collect, or operating conditions to use in order to acquire the most information for improving model predictions at the target experimental scale. The successful applicant will develop original methodologies and have expertise in numerical optimization, optimal experimental design, active learning and Bayesian optimization, sequential decision-making and information theory, adaptive sampling and surrogate-based optimization, and statistical design of experiments. The ability to address challenges arising from applications with noisy, messy, missing or sparse data, and dealing with models that have uncertainty is a must.

 

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Basic Qualifications

Must be a recent PhD graduate within the last three years.

* Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• Demonstrated experience in developing optimal experimental design strategies with strong background in selecting experiments to maximize information, identify parameters, distinguish hypotheses, or improve model predictions.
• Demonstrated experience in developing active learning and sequential design methods for iteratively selecting new data points based on what has already been learned.
• Demonstrated experience in Bayesian optimization, including development of acquisition functions, surrogate models, and sequential optimization, ideally beyond standard black-box optimization.
• Demonstrated experience with multi-fidelity, multi-modal, multi-source sampling including making optimal decisions on where to sample, what conditions to test, which scale (lab to pilot) or level of fidelity to conduct an experiment.
• Understanding of information theory and decision theory with deep understanding of information gain, value of information, and the relationship between uncertainty reduction and practical decisions.
• Understanding of constrained and cost-aware sampling methods including accounting for experiment cost, time, feasibility, safety, and operational constraints when selecting experiments as well as experience modeling constrained and multi-objective optimization problems.
• Demonstrated experience in scientific computing and optimization including the ability to develop and implement efficient algorithms for sequential decision-making.
• Excellent communication skills
• Experience working in multi-disciplinary teams and collaborative software development
• Experience with machine learning and AI.
• Demonstrated experience with python and version control

Preferred Qualifications

·       Self-motivated, creative, and independent thinker

·       Open-minded and eager to learn about new application challenges

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Job Application Submission Window

The anticipated closing window for application submission is up to 30 days and may be extended as needed.

Annual Salary Range (based on full-time 40 hours per week)

Job Profile: Postdoctoral Researcher / Annual Salary Range: $76,600 - $126,400

NLR takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.

Benefits Summary

Benefits include 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; and paid holidays. NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement.

* Based on eligibility rules

Badging Requirement

NLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation.

Drug Free Workplace

NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.

If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.

Submission Guidelines

Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.

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Equal Opportunity Employer

All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.

Reasonable Accommodations

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