Posting Title
Graduate (10-12 weeks) Intern - AI/ML for Cybersecurity .
Location
CO - Golden .
Position Type
Intern (Fixed Term) .
Hours Per Week
40 .
Working at NLR
NLR 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 the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR 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 NLR, 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
The intern will conduct hands-on research at the intersection of machine learning, statistics, and critical energy infrastructure security. The position focuses on enhancing the reliability, transparency, and operator-readiness of AI-driven cybersecurity tools used to secure power grid substations and physical assets, with a primary focus on explainability and robust uncertainty bounds.
· Explainable AI (XAI): Design and implement XAI methods (e.g., attribution maps, rule extraction, root-cause analyses) to make complex ML models transparent and auditable for control-room operators and system administrators.
· Confidence & Uncertainty Quantification: Apply statistical techniques and frameworks (e.g., conformal prediction, Bayesian neural networks, or ensemble methods) to quantify model confidence and measure predictive uncertainty in real-time.
· Grid Dataset Analysis: Analyze cyber-physical power system security datasets (such as 9-Bus system baseline and cyber-physical anomaly runs) to extract feature patterns and validate anomaly detection model behavior.
· Model Safety & Robustness: Explore machine learning safety techniques, including model steering, to ensure grid security models behave predictably under distribution shifts or input corruptions.
· Visualization & Alert Design: Support research into visualization methods and alert design that present explainability and confidence metrics clearly to operators, assisting with alert prioritization.
· Collaboration & Reporting: Partner with cybersecurity researchers and AI development teams to integrate explainability modules, document results, and compile guidelines for deploying reliable AI models.
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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.
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