Posting Title

Graduate (Summer or Year Round) Intern - Machine Learning Applications for Cyber-Physical Power System Operations

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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 Grid Automation and Controls group at NREL focuses on conducting research projects to improve the power systems operations. We develop cutting edge solutions and work closely with industry and utility partners to enhance grid reliability, resilience, and security. To learn more about the work this team does, please click here Power Systems Operations and Controls | Grid Modernization | NREL.

Our team is looking for a year-round intern (summer intern is also acceptable) who has a strong technical background in machine learning (ML) and artificial intelligence (AI) applications in cyber-physical power systems, ideally on the distribution grid with distributed energy resources (DERs). The intern will work on projects developing learning-based analytics, and cyber-attack-resilient control strategies and optimizations for power distribution systems.

Job duties may include but are not limited to:

• Modeling power distribution systems using Open DSS or similar tool
• Developing and implementing control strategies for power distribution systems
• Applying machine learning/AI techniques to develop cyber-attack-resilient control strategies
• conducting literature review, identifying research gaps and summarizing new research opportunities
• Conduct research independently in addition to collaborating with the project PI and other researchers on the project team

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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

• Must have completed a bachelors degree in power systems engineering, electrical engineering, or other relevant areas
• Currently enrolled Ph.D. students in power systems engineering, electrical engineering, or