National Renewable Energy Laboratory Graduate Intern (Year-Round) – Machine Learning, Optimization, and Control for Smart Buildings in Golden, Colorado
Graduate Intern (Year-Round) – Machine Learning, Optimization, and Control for Smart Buildings
CO - Golden
Intern (Fixed Term)
Hours Per Week
The Residential Buildings Research Group in NREL’s Buildings and Thermal Sciences Center has an opening for Graduate Intern (Year-Round) 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 resilience and renewable integration, and perform large-scale simulation on NREL’s high-performance computing systems.
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
Required Education, Experience, and Skills
Must be enrolled as a full-time student in a relevant Master or Ph.D. 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.
Ideal candidates will have a background and expertise in one or more of the following topics:
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, load disaggregation, occupancy prediction, time series analysis, etc.
Good programming and data analytic skills in MATLAB, Python, R, or similar languages
Excellent writing, interpersonal and communication skills
Must be enrolled as a full-time student in a Bachelor's, Master's or PhD degree 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.
•You will need to upload 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.
Additional Required Qualifications
Strong programming and data analytic skills in MATLAB, Python, R, or similar languages
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 as well as reinforcement learning, deep learning, or transfer learning with applications in energy system controls
Experience with resilient operation of building energy systems and microgrid
Experience with transactive control, distributed control, or hierarchical control of complex engineering systems
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.
NREL is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
EEO is the Law at http://www.dol.gov/ofccp/regs/compliance/posters/ofccpost.htm | Pay Transparency Nondiscrimination at https://www.dol.gov/ofccp/pdf/pay-transpEnglishunformattedESQA508c.pdf | Reasonable Accommodations at http://www.nrel.gov/careers/employment-policies.html
E -Verify www.dhs.gov/E-Verify |For information about right to work, click here at http://www.justice.gov/sites/default/files/crt/legacy/2013/08/13/FinalOSCPosterEN08012013.pdf for English or here at http://www.justice.gov/crt/file/813271/download for Spanish.
E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.
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.
NREL is subject to Department of Energy (DOE) access restrictions. All candidates must be authorized to access the facility per DOE rules and guidance within a reasonable time frame for the specified position in order to be considered for an interview. DOE rules for site access during the interview process are the same regardless of whether the candidate is interviewed on-site, off-site, or via telephone or videoconference. Additionally, DOE contractor employees are prohibited from participating in certain Foreign Government Talent Recruitment Programs (FGTRPs). If a candidate is currently participating in an FGTRP, they will be required to disclose their participation after receiving an offer of employment and may be required to disengage from participation in the FGTRP prior to commencing employment. Any offer of employment is conditional on the ability to obtain work authorization and to be granted access to NREL by the Department of Energy (DOE). We understand that COVID-19 may have caused delays or closures at offices, consulates, and embassies. However, NREL cannot make exceptions to work authorization and access requirements, and exceptions to these requirements are not being made for COVID-19 related delays.
Please review the information on our Hiring Process at https://www.nrel.gov/careers/hiring-process.html website before you create an account and apply for a job. We also hope you will learn more about NREL at https://www.nrel.gov/about/ , visit our Careers site at https://www.nrel.gov/careers/ , and continue to search for job opportunities at https://nrel.wd5.myworkdayjobs.com/NREL at the lab.