Graduate Intern

Posting Title

Graduate Intern – Machine Learning - Solar Forecasting

.

Location

CO - Golden

.

Position Type

Intern (Fixed Term)

.

Hours Per Week

40

.

Working at NREL

NREL is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.

Join NREL, where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NREL stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.

At NREL, you’ll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

Job Description

NREL’s Energy Systems Optimization and Control team is thrilled to announce an exciting opportunity for a full-time graduate engineering intern with experience in machine learning, time series forecasting, and solar modeling. This is your chance to be at the forefront of energy innovation, working alongside a dynamic, multidisciplinary team of experts from NREL and its collaborators. As a graduate intern, you will dive into a groundbreaking project, developing and implementing cutting-edge AI algorithms for real-time solar forecasting. Your primary mission will be to leverage your expertise in artificial intelligence and statistics to revolutionize energy forecasting. Your deep knowledge of statistical/machine learning, solar forecasting, time series modeling, and inverter analysis will be the driving force behind your success in this role. This full-time position offers the flexibility of optional remote work.

Key Responsibilities:

• Innovate and Optimize: Build best-in-class models for inverter-level and plant-level solar forecasting with calibrated uncertainty, using RNN, diffusion models, and graph models
• Implement and Impact: Bring your algorithms to life for industry partners, making tangible improvements in solar forecasting
• Lead and Collaborate: Manage our project GitHub repository for experiment tracking and code versioning, ensuring seamless collaboration with partners and code excellence
• Share Your Discoveries: Present your groundbreaking results and key findings at workshops, conferences, and in high-quality journals, positioning yourself as a thought leader in the field

.

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

• Completed a Bachelor's degree and either have completed a master's degree or be enrolled in a masters or PhD degree in in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or a related analytical domain
• Demonstrated knowledge and experience in Python and its related libraries, such as TensorFlow, Keras, and Pytorch
• Demonstrated experience in time series forecasting, computer vision, and scenario generation
• A comprehensive understanding of uncertainty quantification.
• Demonstrated experience documenting and presenting results in presentations, papers, and or publications

Preferred Qualifications

• Hands-on experience in energy related time series forecasting, such as participating in energy forecasting competitions

• Experience in multi-modal machine learning

• Knowledge about PV plants, PV inverters, and PV control

• A track record of producing high quality research papers

.

Job Application Submission Window

The anticipated closing window for application submission is up to 30 days and may be extended as needed.

Annual Salary Range (based on full-time 40 hours per week)

Job Profile: / Annual Salary Range: $50,200 - $80,300

NREL takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.

Benefits Summary

Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). NREL employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.

* Based on eligibility rules

Badging Requirement

NREL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation. Intern assignments extending beyond six months will be subject to this requirement.

Drug Free Workplace

NREL is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.

If you are offered employment at NREL, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.

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.

.

Equal Opportunity 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, domestic partner 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.

Reasonable Accommodations

E-Verify www.dhs.gov/E-Verify For information about right to work, click here for English orhere 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.