National Renewable Energy Laboratory · 1 day ago

Graduate (Summer) Intern - AEG

Remote Site

Internship

Hybrid

Intern/New Grad

$49K/yr - $79K/yr

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Clean EnergyCleanTech

Actively Hiring

No H1B

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Responsibilities

Develop algorithms and simulation tools for autonomous energy grids.

Contribute to the Autonomous Energy Systems project.

Work towards the development of scalable, distributed, and data-driven optimization algorithms for power systems.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Optimization theoryMachine learningAlgorithms developmentProgramming skillsOptimal power flowUnit commitmentConvex optimizationPythonMATLABOperations principlesControls principlesOptimization principlesCloud computingHPCGitHubDistributed algorithmsMixed-integer linear programming

Required

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.

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.

The successful candidate should have completed a bachelor’s degree and be enrolled in a PhD program studying electrical engineering, computer science, or other systems engineering fields.

General knowledge or experience with optimal power flow and unit commitment.

Demonstrated understanding of convex optimization.

Demonstrated knowledge or experience with Machine learning.

Demonstrates broad understanding and wide application of principles, theories, and concepts in operations, controls, and optimization of power systems.

Proficiency in one or more of the following programming languages: Python, MATLAB.