About the role#
TRIUMF is seeking a student to conduct a feasibility study on using machine learning and AI for predictive and preventative maintenance on power supplies. This role involves analyzing data from current control systems and building tools to monitor equipment performance. You will work on real-world engineering data and have the opportunity to develop machine learning algorithms.
What you'll do#
- Analyze data from current control systems, including work permits and logs.
- Build a system to process historical maintenance records.
- Create a standardized database for equipment failures, symptoms, causes, and repairs.
- Develop a system to record live data critical for equipment performance.
- Create a dashboard to present data and highlight anomalies.
- Research AI and machine learning tools for maintenance applications.
- Create a proof-of-concept for project feasibility.
- Develop machine learning algorithms.
What you'll need#
- Current enrollment as an undergraduate student at an accredited post-secondary institution.
- Background in electrical engineering, computer engineering, or machine learning and neural networks.
- Experience with data analysis.
- Proficiency in Python and web development.
- Experience with machine learning or AI development.
Location & details#
- Location: Vancouver, British Columbia, Canada.
- Term: Winter 2027 (January 04, 2027 to April 30, 2027).
- Modality: On-site.
- Employment type: Full-time.
- Compensation: This is a paid position.
About TRIUMF
TRIUMF is the particle accelerator center for Canada. Founded in 1968 and based in Vancouver, the organization employs between 201 and 500 people. It conducts research in particle and nuclear physics, isotope science, and materials science. The facility collaborates with universities to develop technologies and study fundamental particles.
How to get in at TRIUMF
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