Undergraduate Intern - Data Science for Autonomous Experimentation

Location: Golden, CO

Position Type: Fixed-Term Internship

Hours Per Week: 10

About NREL

The National Renewable Energy Laboratory (NREL) is located at the foothills of the Rocky Mountains in Golden, Colorado. NREL is the nation's premier laboratory for research and development of renewable energy and energy efficiency technologies.

From your first day at NREL, you’ll work alongside coworkers committed to the same mission: saving the planet. Join an organization that values a supportive, inclusive, and flexible work environment. Engage with our ten employee resource groups, numerous employee-driven clubs, and various learning and professional development classes.

NREL supports inclusive, diverse, and unbiased hiring practices that foster creativity and innovation. Through collaboration with organizations focusing on diverse talent pools and underrepresented demographics, we provide an inclusive application and interview process. Our Talent Acquisition team strives to hear all voices equally. We aim to attract a highly diverse workforce and create a culture where every employee feels welcomed, respected, and able to be their authentic self.

Our planet needs us! Learn about NREL’s critical objectives and see how we are focused on saving the planet.

Job Description

NREL seeks a motivated and talented Data Science Intern to join the Analytical Microscopy and Imaging Sciences Group. The ideal candidate will have an interest in applying computer vision, machine learning, and programming skills to analyze and interpret complex electron microscopy data. This is an exciting opportunity to work at the intersection of materials science and data science, contributing to research that may reshape our understanding of materials for clean energy and next-generation computing.

Key Responsibilities

• Develop and implement machine learning and computer vision algorithms to analyze electron microscopy images and videos for autonomous experimentation.
• Collaborate with researchers to design and conduct experiments to gather data for analysis.
• Participate in the development of data analysis pipelines and workflows.
• Engage in weekly remote meetings and conduct analysis remotely.
• Contribute to code development on NREL-hosted GitHub repository.
• Present and document findings in regular updates to our team.

Highest Needs

• Pursuing or recently completed a degree in Data Science, Materials Science, Mechanical Engineering, or a related field.
• Strong programming skills in Python or a similar language.
• Excellent analytical and problem-solving skills.

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 a master’s degree and enrolled as a 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 the position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• Familiarity with image processing and computer vision techniques.
• Experience designing automated, autonomous, and/or robotic systems.
• Familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch).
• Python programming skills.

Preferred Qualifications

• Experience with electron microscopy or other materials characterization techniques.
• Interest and aptitude in experimental work with large interdisciplinary teams.
• Excellent communication and presentation skills