AI Validation of Electrical Design – Phase I - QC-199

Preferred Disciplines: Software/Computer Engineering (Masters or undergraduate)
Project length: 4-6 months (1 unit)
Approx. start date: September 2019
Location: Montreal, QC
No. of Positions: 1
Preferences: None
Company: CAE Inc – Hardware Engineering

About Company:

CAE is a global leader in training for the civil aviation, defence and security, and healthcare markets. Backed by a 70-year record of industry firsts, we continue to help define global training standards with our innovative virtual-to-live training solutions to make flying safer, maintain defence force readiness and enhance patient safety.

Summary of Project:

In Electrical Design at CAE, there are trends and patterns that repeat from project to project and within a project. The idea is to use (AI, Machine Learning, Big Data, etc…) concepts to analyse the existing data to be able to guide and validate designers in new projects and detect abnormalities and errors. We will target specific areas to analyse: Connections, Cable markers, cable lengths.

Research Objectives/Sub-Objectives:

  • Analyse CAE electrical data and propose approach for validation/guidance.
  • Document all aspects of the solution
  • Provide proof of concept application/code that does validation/guidance.


    • CAE to provide extracts of data in .XLSX format (unable to provide entire data)
    • Understand data relationship with analysis requirements
    • Identify method(s), tool(s), architecture to achieve analysis requirements
    • Conceptual draft
    • Develop application
    • Documentation

    Expertise and Skills Needed:

    • Basic knowledge in electrical design (mostly type of signals: video, audio, power, IO, etc…)
    • Knowledge of Data Analysis methods and AI
    • Good communication skills, a good work ethic and team spirit

    For more info or to apply to this applied research position, please

    1. Check your eligibility and find more information about open projects.
    2. Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform or directly to Marie-Laure de Boutray