Posting Title

Graduate PhD Student Intern (Summer) – Mathematical Optimization

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Location

CO - Golden

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Position Type

Intern (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 AI, Learning and Intelligent Systems (ALIS) Group in the NLR Computational Science Center has an opening for a graduate student researcher in Mathematical Optimization for large-scale power systems planning. They will deploy developed optimization algorithms on DOE high-performance computing systems. The researcher will develop mathematically sound approaches for transmission and capacity expansion as applied to the bulk electricity systems to enhance economics, reliability, resilience, and security of bulk electric systems.

Responsibilities include:

• Develop and implement mathematically sound approaches for transmission and capacity expansion using distributed optimization methods on NLR’s HPC
• Collaborate with NLR researchers to assess tradeoffs between model detail and computational time
• Process and visualize results to inform algorithmic design
• Author, present and assist in the preparation of technical papers, reports and conference proceedings on topics related to power systems planning

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Basic Qualifications

Minimum of a 3.0 cumulative grade point average.

Undergraduate: Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution.
Post Undergraduate: Earned a bachelor’s degree within the past 12 months. Eligible for an internship period of up to one year.

Graduate: Must be enrolled as a full-time student in a master’s degree program from an accredited institution.
Post Graduate: Earned a master’s degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate + PhD: Completed master’s degree and enrolled as PhD student from an accredited institution.

Please Note:
• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.

* Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• Currently pursuing a PhD in applied mathematics, industrial engineering, chemical engineering, management science, operations research, or a related discipline
• Demonstrated experience with algebraic modeling, including the use of modeling tools such as Pyomo, JuMP, GAMS, AMPL, CPLEX, Gurobi etc.
• Demonstrated experience implementing algorithms with Python, Julia, or other major language

Preferred Qualifications

• Experience with Pyomo and/or JuMP.

• Experience with commercial and/or open-source optimization solvers (e.g., Gurobi, HiGHS, IPOPT).

• Experience with developing custom math-programming algorithms tailored to specific problems.

• Experience working with cross-disciplinary research teams

• Experience with mpi-sppy and/or progressive hedging

• Candidates should have demonstrated interest or experience in power systems planning and/or operations.

• Experience with publishing

• Experience with HPC workflows, bash script,