Internship - Condition Monitoring and Fault Diagnosis

About the role

The intern will contribute to the development of advanced monitoring and diagnostic technologies, with applications that may include electric motors and motor-driven systems.

Responsibilities

• Contribute to the development of advanced monitoring and diagnostic technologies
• Work on fault diagnosis, prognosis, and health management
• Develop models and analyze data related to electric machines
• Apply machine learning techniques including transfer learning and domain adaptation for fault diagnosis

Requirements

• Ph.D. student with a solid background and publication record in fault diagnosis, prognosis, and health management; electric machine modeling and data analysis; machine learning techniques
• Strong programming skills in Python and familiarity with frameworks such as PyTorch
• Experience with modeling and analysis of electric machines is highly desirable

Qualifications

• Senior Ph.D. students in related fields (e.g., Electrical Engineering, Mechanical Engineering, Applied Physics) are encouraged to apply

Skills

• Advanced knowledge in fault diagnosis, prognosis, and health management
• Familiarity with electric machine modeling and data analysis
• Proficiency in Python and PyTorch
• Experience with machine learning techniques including transfer learning and domain adaptation

Benefits

• Relocation stipend
• Covered travel to and from MERL
• Monthly Charlie Card for local commuting
• Invitation to participate in weekly social gatherings and professional development opportunities
• Eligibility for health insurance coverage after 90 days
• Immigration support for qualified candidates

Pay

The pay range for this internship position will be 6-8K per month.

Schedule

Start date for this internship is flexible and the duration is 3 months.