Graduate Intern – AI/ML for Cybersecurity

Advanced Energy Systems - Colorado School of Mines & NLR

. Conduct hands-on research at the intersection of machine learning, statistics, and critical energy infrastructure security .

Posted 10/15/2026internshipRemote • Colorado • United StatesEntry Level💰 $51,200 - $81,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in machine learning, statistics, and cybersecurity, with a focus on explainable AI methods and statistical uncertainty modeling. Proficient in Python and experienced with frameworks such as PyTorch and TensorFlow, while also capable of analyzing cyber-physical power system security datasets.

Highest-signal resume keywords

Python ProficiencyExplainable AI (XAI)Machine Learning Safety TechniquesStatistical Uncertainty ModelingHands-On Experience with PyTorch

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical TechniquesExplainable AI MethodsBayesian Neural NetworksEnsemble MethodsConformal PredictionRoot-Cause AnalysisCyber-Physical Security AnalysisLarge Language Models (LLMs)Deep Neural Networks

Tools & Technologies

PyTorchTensorFlowHugging FaceCaptumSHAPLIME

Certifications & Qualifications

Personal Identity Verification (PIV) Card

Industry Keywords

CybersecurityEnergy Infrastructure SecurityPower Grid SubstationsAI-Driven Cybersecurity ToolsMulti-Sensor Data FusionGrid Anomaly Detection

Tech Stack

Tools & technologies

Cyber SecurityPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Conduct hands-on research at the intersection of machine learning, statistics, and critical energy infrastructure security
Enhance the reliability, transparency, and operator-readiness of AI-driven cybersecurity tools for power grid substations and physical assets
Design and implement explainable AI methods, including attribution maps, rule extraction, and root-cause analyses
Apply statistical techniques such as conformal prediction, Bayesian neural networks, and ensemble methods to quantify model confidence and predictive uncertainty in real time
Analyze cyber-physical power system security datasets, including 9-Bus system baselines and cyber-physical anomaly runs
Explore machine learning safety techniques, including model steering, for predictable behavior under distribution shifts or input corruptions
Support visualization and alert design for explainability and confidence metrics and alert prioritization
Partner with cybersecurity researchers and AI development teams
Integrate explainability modules, document results, and compile deployment guidelines for reliable AI models

Requirements

What you’ll need

Minimum cumulative GPA of 3.0
Undergraduate applicants must be enrolled full-time in a bachelor's degree program from an accredited institution
Post-undergraduate applicants must have earned a bachelor's degree within the past 12 months
Graduate applicants must be enrolled full-time in a master's degree program from an accredited institution
Post-graduate applicants must have earned a master's degree within the past 12 months
Graduate + PhD applicants must have completed a master's degree and be enrolled as a PhD student from an accredited institution
Currently pursuing or completed a Bachelor's, Master's, or Ph.D. in Computer Science, Cybersecurity, Electrical Engineering, AI, Applied Mathematics, or Statistics
Must meet educational requirements prior to employment start date
Applicants must upload official or unofficial school transcripts
Letter of recommendation required if selected
Strong proficiency in Python
Hands-on experience with PyTorch, TensorFlow, or Hugging Face
Substantial coursework or project experience in Explainable AI (XAI), machine learning safety, confidence calibration, or statistical uncertainty modeling
Practical experience working with Large Language Models (LLMs), Foundation Models, or other complex deep neural networks
Ability to obtain and maintain a federal Personal Identity Verification (PIV) card for assignments extending beyond six months, including a favorable background investigation
Must pass a pre-employment drug test, which may include marijuana unless prohibited by state or local law
Must submit an application, cover letter, and resume for each position
Preferred: active research/publications in specified top-tier conferences
Preferred: experience with Captum, SHAP, LIME, and uncertainty calibration methods
Preferred: familiarity with multi-sensor data fusion, power systems modeling, or grid anomaly detection
Preferred: contributions to open-source AI transparency, reliability, or security libraries

Benefits

Comp & perks

Medical, dental, and vision insurance
403(b) Employee Savings Plan with employer match
Sick leave where required by law
Potential eligibility for performance-, merit-, and achievement-based monetary awards
Potential eligibility for relocation expense reimbursement
Robust professional development opportunities
Competitive benefits package designed to support career and well-being