Posting Title

Graduate (Summer) Intern – Optimization for Advanced Energy

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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 NREL

The National Renewable Energy Laboratory (NREL), located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for research and development of renewable energy and energy efficiency technologies.

Join a team of world-class scientists, engineers, and visionaries dedicated to shaping the world’s energy future through cutting-edge research and innovation. From our vision to our NREL community, we are unique in the research community. We are focused on impact. From our work in basic sciences to systems engineering, analysis, demonstration, and deployment, we are focused on solving market-relevant problems that result in advanced, secure, reliable, and affordable energy systems. We are trusted clean energy leaders, developing cost-saving solutions that make U.S. industries more competitive, and support job creation and economic growth across rural and urban communities.

At NREL, we offer a unique, mission-driven work environment with cutting-edge facilities and multidisciplinary research teams. NREL's environment offers strong partnerships with industry, academia, and other national laboratories, as well as professional development opportunities and a competitive benefits package for employees.

Learn about NREL’s critical objectives: NREL's Mission and Vision.

Job Description

The AI, Learning and Intelligent Systems Group in the NREL Computational Science Center has an opening for a graduate student researcher to work on decomposition methods for large scale optimization problems in the energy sector. The ideal candidate will assist in developing a suite of math programming optimization-based decomposition techniques related to progressive hedging, the alternating directions method of multipliers, and Bender’s decomposition.

We are looking for a dynamic, motivated researcher with a strong technical background and an interest in the mission of NREL. The successful candidate will have strong applied math expertise in optimization theory and algebraic modeling.

Responsibilities include:

• Reading technical literature to survey the state of the art in decomposition methods for large-scale optimization problems  
• Implementing decomposition techniques from the literature into NREL optimization code bases
• Understanding and utilizing software frameworks for algebraic modeling such as Pyomo or JuMP
• Author, present and assist in the preparation of technical papers, reports, and conference proceedings.

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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

Candidates must have:

• Experience implementing decomposition techniques.
• Basic understanding of algebraic modeling, such as Pyomo or JuMP.
• Interest in large scale optimization for energy systems.
• Experience in coding, particularly in Python or Julia.

Preferred Qualifications

Experience in or understanding of parallel programming.

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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.

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