Posting TitleGraduate (3-12 month) Intern - Machine Learning Applications to Power System Operations.LocationCO - Golden.Position TypeIntern (Fixed Term).Hours Per Week40.Working at NRELThe 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.From day one at NREL, you’ll connect with coworkers driven by the same mission to save the planet. By joining an organization that values a supportive, inclusive, and flexible work environment, you’ll have the opportunity to engage through our ten employee resource groups, numerous employee-driven clubs, and learning and professional development classes.NREL supports inclusive, diverse, and unbiased hiring practices that promote creativity and innovation. By collaborating with organizations that focus on diverse talent pools, reaching out to underrepresented demographics, and providing an inclusive application and interview process, our Talent Acquisition team aims to hear all voices equally. We strive to attract a highly diverse workforce and create a culture where every employee feels welcomed and respected and they can be their authentic selves.Our planet needs us! Learn about NREL’s critical objectives, and see how NREL is focused on saving the planet. We invite all interested candidates to apply for this opportunity. While we recognize that job seekers may hesitate if they don’t meet every requirement, we encourage dedicated individuals who meet all the basic and additional required qualifications of the role to submit an application. We value the opportunity to consider those who believe they have the necessary skills and ambition to succeed at NREL.Job DescriptionThis Grid Automation and Controls group at NREL focuses on conducting research projects to improve power systems operations. We develop cutting edge solutions and work closely with industry and utility partners to enhance grid reliability, resilience, and security. Our team is looking for an intern for a 3-12 month period who has strong technical background in machine learning (ML) and artificial intelligent (AI) applications in power systems, ideally on the distribution grid and distributed energy resources (DERs).To learn more about the work this group does, check out the following link: https://www.nrel.gov/grid/distributed-energy-resource-management-systems.htmlJob responsibilities will include but are not limited to:Collaborating with internal and external stakeholders to advance research projectsDeveloping learning-based analytics, controls and optimizations for power systems and DERsContributing and/or leading the writing of research papersThe ideal candidate should be able to conduct research work independently .Basic QualificationsMinimum 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 QualificationsThe successful will have completed an undergraduate or master's degree in computer science, electrical engineering, or related field or be enrolled in a PhD program in these fields. Experienced in one or more ML/AI techniques, such as reinforcement learning, federated learning, natural language learning, graph neural networkHave a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications to complex systemsProficiency in using Python and machine learning/reinforcement learning packagePreferred QualificationsHave research background in power systems, distribution grids, building and vehicle grid integrationsFamiliar with power system modeling and power system optimization Experience in using power system simulation software (e.g. OpenDSS, GridLab-D, CYMDIST) Experience in using high performance computers, Linux systemsThe ideal candidate would be able to work onsite in Golden, Colorado.Job Application Submission WindowThe 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: $42,700 - $68,300NREL 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 SummaryBenefits 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 rulesBadging RequirementNREL 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 WorkplaceNREL 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 GuidelinesPlease 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..EEO PolicyNREL 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, 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.EEO is the Law | Pay Transparency Nondiscrimination | Reasonable Accommodations E-Verify www.dhs.gov/E-Verify For information about right to work, click here for English or here 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.
