Postdoctoral Researcher in Numerical Optimization and Experimental Design

National Laboratory of the Rockies; Golden (CO); On-site; USD 77,000 - 126,000; Full time

Job summary

National Laboratory of the Rockies (NLR) invites applications for a Postdoctoral Researcher in Numerical Optimization and Experimental Design. The role focuses on cross-disciplinary optimization for cross-scale scientific applications, spanning lab to pilot scale.

You will develop methods that decide what experiments to run and which measurements to collect to improve predictions under uncertainty. Applicants should have a recent PhD and strong background in numerical optimization, adaptive

Qualifications

• Must be a recent PhD graduate within the last three years.
• Demonstrated experience in developing optimal experimental design strategies to maximize information.
• Demonstrated experience in active learning and sequential design methods.
• Demonstrated experience in Bayesian optimization and surrogate models.
• Experience with multi-fidelity, multi-source sampling and constraint-aware optimization.
• Strong understanding of information gain and uncertainty reduction in decision making.
• Proficient in scientific computing and implementing sequential decision-making algorithms.
• Excellent communication and teamwork skills.
• Experience with machine learning and AI.
• Proficient in Python and version control.

Responsibilities

• Develop optimization and sampling methods that leverage model predictions and uncertainties.
• Determine which experiments to run and measurements to improve model predictions at target scales.
• Create original methodologies in numerical optimization, active learning, and Bayesian optimization.
• Address challenges with noisy or sparse data and with uncertain models.

Skills

Experimental design

Active learning

Bayesian optimization

Information theory

Multi-fidelity sampling

Scientific computing

Python

Version control

Communication

Education

PhD in a relevant field

Tools

Python

Git

Job description

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

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.

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 (https://www.nlr.gov/careers/employment-policies)

E-Verify

E -Verify www.dhs.gov/E-Verify For information about right to work, click here (http://www.justice.gov/sites/default/files/crt/legacy/2013/08/13/FinalOSCPosterEN08_01_2013.pdf) for English or here (http://www.justice.gov/crt/file/813271/download) for Spanish.

E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.

The National Laboratory of the Rockies (NLR) is the U.S. Department of Energy's primary national laboratory for critical minerals, energy innovation, and energy security. With locations in Golden and Boulder, Colorado, Fairbanks, Alaska, and a satellite office in Washington, D.C., NLR bridges foundational research with practical applications to develop and bring to scale new energy materials and technologies that lower energy costs, drive economic growth, and deliver abundant and reliable energy.

NLR is subject to Department of Energy (DOE) access restrictions. All candidates must be authorized to access the facility per DOE rules and guidance within a reasonable time frame for the specified position in order to be considered for an interview and for hiring. DOE rules for site access during the interview process depend on whether the candidate is interviewed on-site, off-site, or via telephone or videoconference. 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. Additionally, DOE contractor employees are prohibited from participating in certain Foreign Government Talent Recruitment Programs (FGTRPs). If a candidate is currently participating in an FGTRP, they will be required to disclose their participation after receiving an offer of employment and may be required to disengage from participation in the FGTRP prior to commencing employment. Any offer of employment is conditional on the ability to obtain work authorization and to be granted access to NLR by the Department of Energy (DOE).

Drug Free Workplace

NLR is committed to maintaining a drug-free workplace in accordance with 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.

Please review the information on our Hiring Process (https://www.nlr.gov/careers/hiring-process.html) website before you create an account and apply for a job. We also hope you will learn more about NLR (https://www.nlr.gov/about/) , visit our Careers site (https://www.nlr.gov/careers/) , and continue to search for job opportunities (https://nrel.wd5.myworkdayjobs.com/NLR) at the lab.