Research Associate (Engineering/Electrical & Material)
Core
Analyze equipment degradation time-series data and develop forecasting workflows using statistical, machine learning, and deep learning models for semiconductor equipment health monitoring.
Role type
Research Associate (Engineering/Electrical & Material)
Builds
Predictive maintenance and reliability studies for semiconductor equipment
Domain
Semiconductor manufacturing / Equipment reliability
Deliverable
production ML models
Required skills
Python, time-series forecasting, statistical modeling, machine learning, deep learning, data preprocessing, signal smoothing, noise analysis, model evaluation
Preferred skills
semiconductor research experience, postgraduate degree in Materials Science/Electrical Engineering/Physics
Technologies
Python
Responsibilities
Develop and apply time-series forecasting methods for semiconductor equipment health monitoring; Analyze equipment degradation data to support predictive maintenance and reliability studies; Implement and compare statistical, machine learning, and deep learning forecasting models; Perform data preprocessing, signal smoothing, and noise analysis on real-world equipment data; Evaluate model performance using appropriate forecasting metrics and visual analysis; Interpret results to determine when forecasting is meaningful and when data limitations apply
Seniority
Mid-level, hands-on IC