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Job Summary

The Undergraduate Research Assistant will contribute to an NSF-funded research project developing modular, extensible laboratory environments that integrate Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS) into cybersecurity education. The project builds full-cycle labs that move from attack/data generation to feature engineering, model development, and deployment within containerized environments

The student will participate in converting peer-reviewed cybersecurity research into hands-on, reproducible educational labs using Docker, virtual machines, and AI/ML pipelines.

This position provides opportunities for:

• Research experience
• Conference co-authorship (e.g., IEEE FIE, ACM SIGCSE)
• Publication exposure
• Graduate school preparation

Responsibilities*

• Assist in developing full-cycle AI/ML cybersecurity labs:
  • Data/attack generation
  • Data preprocessing and labeling
  • Feature engineering and model development
  • Testing and deployment
• Implement ML models (supervised, unsupervised, deep learning)
• Develop and test Python notebooks for lab exercises
• Support containerization using Docker and virtualized environments
• Document lab procedures and contribute to public dissemination repository
• Assist in internal assessment data collection and evaluation
• Participate in weekly research meetings and technical reviews

Required Qualifications*

• Undergraduate student majoring in Cybersecurity, Computer Science, Data Science, or related field
• Completed coursework in:
  • Python programming
  • Introductory cybersecurity
  • Data structures or algorithms
• Familiarity with:
  • Basic machine learning concepts
  • Linux command line
    

Desired Qualifications*

Experience with:

• Docker or virtualization tools
• Scikit-learn, TensorFlow, or PyTorch
• Network traffic analysis
• Git/GitHub

Interest in graduate school or research career

Modes of Work

Hybrid
The work requirements allow both onsite and offsite work and an employee has an expected recurring onsite presence. On occasion, the employee may be required and must be available to work onsite more frequently if necessitated by unit leadership or their designee and/or the job requirements.

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

Background Screening

The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.

Application Deadline

Job openings are posted for a minimum of three calendar days. The review and selection process may begin as early as the fourth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

U-M EEO Statement

The University of Michigan is an equal employment opportunity employer.