Are you passionate about machine learning (ML), signal processing, and solving real-world problems in industrial systems?
Description:
We're looking for a motivated student with experience in unsupervised anomaly detection (e.g., One-Class SVM, Isolation Forest) to work on motor fault detection systems.
This involves using vibration analysis, Fourier Transforms (FFT), and other signal processing techniques to detect mechanical and electrical faults in industrial motors.
Ideal candidate would have:
- Experience with unsupervised ML models (OCSVM, Isolation Forest, etc.)
- Familiarity with vibration data analysis and condition monitoring
- Hands-on experience in Python, Azure Databricks, or other cloud platforms
- A keen interest in predictive maintenance and industrial applications
If this sounds like something you’re excited about, let’s connect!
Details:
Job Type:
Paid internship.
Time:
2 hours / day
2 days per week (flexible schedule)
Pay rate:
$25-$50/h (based on experience)
Pay frequency:
weekly
Project Duration:
4 weeks (1 month) with potential for growth.
How we work:
Environment:
Remote/Hybrid preferred if you're in NYC.
Meetings:
There is one planing session meeting every week on Monday - flexible time between morning or afternoon.
It alternates between in-person and remote - every other one is an in-person planning session.
(e. g. If week 1 is In-Person, Week 2 is Remote, Week 3 is In-Person)
Communication:
We use Slack, Slack huddles and Google meets for any additional meetings outside of onboarding and support.