This is a Co-op Opportunity for engineering students to build a Python-based AI system for real-time anomaly detection in an endurance test lab for electro-mechanical brake systems.
- Project: Develop a system to analyze live sensor data predict component wear and flag potential failures.
- Core Responsibilities:
- Develop software requirements to guide application development.
- Use Python libraries (Pandas NumPy Scikit-Learn) to process data.
- Train machine learning models for anomaly detection.
- Visualize data with Matplotlib.
- Additional Responsibilities:
- Update Endurance Lab Windows CVI code as required.
- Integrate monitoring code into the Endurance Test program.
- Work-scope options:
- Full-time Summer semester
- Full-time Fall semester
- Part-time (20 hours/week) work/school summer and/or fall
Qualifications :
- Must be pursuing a Bachelors or Masters degree from a 4 year university preferably in Electrical Engineering or Computer Engineering.
- Junior senior or 1st-year graduate student preferred.
- Preferred Skills:
- Foundational experience in a programming language such as Python C C etc.
- Strong interest in AI and data science.
- Self starting results oriented flexible team player.
- Demonstrates a strong sense of teamwork.
- Ability to coordinate and manage multiple tasks simultaneously.
- Meet deadlines on assigned tasks utilize project timeline tools effectively communicate goals and results.
Additional Information :
Equal Opportunity Employer including disability / veterans.
Indefinite U.S. work authorized individuals only. Future sponsorship for work authorization is not available.
Compensation & Benefits:
- Competitive hourly wage
- Paid Holidays
- Seniority accrual and guaranteed wage increases when returning for multiple terms
- Rewarding learning experience
- Post-graduation competitive advantage gained through practical work experience
Remote Work :
No
Employment Type :
Intern
This is a Co-op Opportunity for engineering students to build a Python-based AI system for real-time anomaly detection in an endurance test lab for electro-mechanical brake systems.Project: Develop a system to analyze live sensor data predict component wear and flag potential failures.Core Responsib...
This is a Co-op Opportunity for engineering students to build a Python-based AI system for real-time anomaly detection in an endurance test lab for electro-mechanical brake systems.
- Project: Develop a system to analyze live sensor data predict component wear and flag potential failures.
- Core Responsibilities:
- Develop software requirements to guide application development.
- Use Python libraries (Pandas NumPy Scikit-Learn) to process data.
- Train machine learning models for anomaly detection.
- Visualize data with Matplotlib.
- Additional Responsibilities:
- Update Endurance Lab Windows CVI code as required.
- Integrate monitoring code into the Endurance Test program.
- Work-scope options:
- Full-time Summer semester
- Full-time Fall semester
- Part-time (20 hours/week) work/school summer and/or fall
Qualifications :
- Must be pursuing a Bachelors or Masters degree from a 4 year university preferably in Electrical Engineering or Computer Engineering.
- Junior senior or 1st-year graduate student preferred.
- Preferred Skills:
- Foundational experience in a programming language such as Python C C etc.
- Strong interest in AI and data science.
- Self starting results oriented flexible team player.
- Demonstrates a strong sense of teamwork.
- Ability to coordinate and manage multiple tasks simultaneously.
- Meet deadlines on assigned tasks utilize project timeline tools effectively communicate goals and results.
Additional Information :
Equal Opportunity Employer including disability / veterans.
Indefinite U.S. work authorized individuals only. Future sponsorship for work authorization is not available.
Compensation & Benefits:
- Competitive hourly wage
- Paid Holidays
- Seniority accrual and guaranteed wage increases when returning for multiple terms
- Rewarding learning experience
- Post-graduation competitive advantage gained through practical work experience
Remote Work :
No
Employment Type :
Intern
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