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NREL Internship: Machine Learning, Optimization, and Control for Smart Buildings in Golden, Colorado

Posting Title

Internship: Machine Learning, Optimization, and Control for Smart Buildings

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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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Job Description

The Residential Buildings Research Group in NREL’s Buildings and Thermal Sciences Center has an opening for Graduate Intern in smart buildings with emphasis on machine learning, optimization, and control. The researcher will join a highly interdisciplinary team and conduct cutting-edge research on building-to-grid integration. The candidate will collaborate with NREL researchers and external partners to model smart buildings and microgrids, develop novel machine learning and control algorithms to improve building operations and renewable integration, and perform large-scale simulation on NREL’s high-performance computing systems.

Successful candidates will have qualifications in the following categories:

  • Strong background in modeling, simulation, and control of buildings or energy systems

  • Machine learning experience with energy-related applications such as data-driven modeling, load forecasting, time series analysis, etc.

  • Good programming and data analytic skills in MATLAB, Python, R, or similar languages

  • Excellent writing, interpersonal and communication skills

Job Duties

Under the general direction of senior staff, the successful candidate will:

  • Develop novel algorithms and perform exploratory research in the area of building-to-grid integration

  • Collaborate on multi-disciplinary teams with NREL colleagues in building systems, power systems, energy storage, etc.

  • Support senior staff in developing and implementing machine learning, optimization and control algorithms for building systems and distributed energy resources

  • Summarize research results in technical papers, reports and conference proceedings

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Basic Qualifications

Must be enrolled as a full-time student in a degree granting program, or graduated in the past 12 months from an accredited institution. Internship period cannot exceed 12 months past graduation. Minimum of a 3.0 cumulative grade point average. Please Note: You will need to upload unofficial transcripts and a letter of recommendation as part of the application process.

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Additional Qualifications

Preferred Qualifications

Preferred Qualification

Ideal candidates will have a background and expertise in one or more of the following topics:

  • Solid background in classical control theory and control-oriented modeling of cyber-physical systems such as buildings and energy storage systems

  • Research experience on building-to-grid integration and modeling of occupant behavior in buildings

  • Strong optimization skills and hands-on experience with convex and mixed-integer programming tools

  • Deep understanding and demonstrated experience with classical machine learning techniques

  • Experience with learning-based control such as transfer learning and reinforcement learning with applications in energy system controls

  • Prognostics and health management of building systems and other energy systems for extending equipment life and enhancing resilience

  • Experience with distributed control or hierarchical control of complex engineering systems

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Submission Guidelines

Please note that in order to be considered an applicant for any position at NREL you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.

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EEO Policy

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The National Renewable Energy Laboratory (NREL) is a leader in the U.S. Department of Energy’s effort to secure an environmentally and economically sustainable energy future. With locations in Golden and Boulder, Colorado, and a satellite office in Washington, D.C., NREL is the primary laboratory for research, development, and deployment of renewable energy technologies in the United States.

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