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Golden, Colorado, United States
Onsite
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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.
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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* Must meet educational requirements prior to employment start date.
· Education: Currently pursuing or completed a Bachelor’s, Master’s, or Ph.D. in Computer Science, Cybersecurity, Electrical Engineering, AI, Applied Mathematics, or Statistics.
· Programming & ML Frameworks: Strong proficiency in Python; hands-on experience with PyTorch, TensorFlow, or Hugging Face.
· Core Research Competency: Substantial coursework or project experience in Explainable AI (XAI), machine learning safety, confidence calibration, or statistical uncertainty modeling.
· Generative AI / LLMs: Practical experience working with Large Language Models (LLMs) or Foundation Models or other complex deep neural networks.
· Active research/publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, AAAI, IEEE S&P, USENIX Security, or ACM CCS) with a focus on XAI, robust ML, or cyber-physical energy systems.
· Experience with specialized explainability libraries (such as Captum, SHAP, or LIME) and uncertainty calibration methods.
· Familiarity with multi-sensor data fusion, power systems modeling, or grid anomaly detection.
· Contributions to open-source software libraries focusing on AI transparency, reliability, or security.
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The anticipated closing window for application submission is up to 30 days and may be extended as needed.
NLR 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.
* Based on eligibility rules
NLR 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 NLR, 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.
Please note that in order to be considered an applicant for any position at NLR 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.
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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.
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