- •
Expertise in machine learning, time-series analysis, and anomaly detection.
- •
Proficiency in Python and common data science and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch).
- •
Solid understanding of signal processing concepts and hands-on experience with industrial sensor data (e.g., vibration, current, temperature, pressure).
- •
Ability to read, interpret, and apply insights from academic literature and state-of-the-art research in condition monitoring and fault diagnosis.
- •
Experience designing experiments to validate hypotheses and benchmark models.
- •
Strong problem-solving skills and ability to handle noisy, high-dimensional data.
- •
Advanced English.