Graduate Intern – AI/ML for Cybersecurity

This is a fully remote position, open to applicants in Colorado.

📋 Description

• Engage in practical research that merges machine learning, statistics, and the security of critical energy infrastructure.

• Improve the reliability, transparency, and readiness of AI-powered cybersecurity solutions for power grid substations and physical assets.

• Create and implement explainable AI techniques, which include attribution maps, rule extraction, and root-cause analyses.

• Utilize statistical methods such as conformal prediction, Bayesian neural networks, and ensemble techniques to assess model confidence and predictive uncertainty in real-time.

• Examine datasets related to cyber-physical power system security, encompassing 9-Bus system baselines and cyber-physical anomaly assessments.

• Investigate safety strategies in machine learning, including model steering, to ensure predictable behavior amid distribution shifts or input corruption.

• Assist in the design of visualizations and alerts for enhancing explainability, confidence metrics, and alert prioritization.

• Collaborate with cybersecurity researchers and AI development teams.

• Incorporate explainability modules, document findings, and develop deployment guidelines for dependable AI models.

⛳️ Requirements

• A minimum cumulative GPA of 3.0 is required.

• Undergraduate candidates must be enrolled as full-time students in a bachelor's degree program at an accredited institution.

• Post-undergraduate candidates should have obtained a bachelor's degree within the last 12 months.

• Graduate candidates need to be enrolled full-time in a master's degree program at an accredited institution.

• Post-graduate candidates must have earned a master's degree in the past 12 months.

• Graduate and PhD candidates should have completed a master's degree and be enrolled as PhD students at an accredited institution.

• Candidates must be pursuing or have completed a Bachelor's, Master's, or Ph.D. in Computer Science, Cybersecurity, Electrical Engineering, AI, Applied Mathematics, or Statistics.

• Educational prerequisites must be fulfilled before the employment start date.

• Candidates are required to upload official or unofficial academic transcripts.

• A letter of recommendation is mandatory if selected.

• Strong proficiency in Python is essential.

• Practical experience with PyTorch, TensorFlow, or Hugging Face is required.

• Significant coursework or project experience in Explainable AI (XAI), machine learning safety, confidence calibration, or statistical uncertainty modeling is needed.

• Experience with Large Language Models (LLMs), Foundation Models, or other sophisticated deep neural networks is important.

• Ability to acquire and maintain a federal Personal Identity Verification (PIV) card for assignments lasting over six months, including a successful background investigation.

• Candidates must pass a pre-employment drug test, which may include marijuana unless restricted by state or local law.

• An application, cover letter, and resume must be submitted for each position.

• Preferred: active involvement in research or publications in leading top-tier conferences.

• Preferred: experience with Captum, SHAP, LIME, and methods for uncertainty calibration.

• Preferred: knowledge of multi-sensor data fusion, power systems modeling, or grid anomaly detection.

• Preferred: contributions to open-source projects focused on AI transparency, reliability, or security libraries.

🏝️ Benefits

• Medical, dental, and vision insurance.

• 403(b) Employee Savings Plan with employer matching.

• Sick leave as mandated by law.

• Possible eligibility for performance-, merit-, and achievement-based monetary rewards.

• Potential eligibility for reimbursement of relocation expenses.

• Extensive professional development opportunities.

• Competitive benefits package aimed at supporting career goals and well-being.