Graduate Intern - Machine Learning - Solar Forecasting

Nrel-posted 27 days ago

Full-time • Intern

Remote • Golden, CO

1,001-5,000 employees

Administrative and Support Services

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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.

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

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.
Must meet educational requirements prior to employment start date.
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

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

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.

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