Research Associate – Time-Series Forecasting for Semiconductor Equipment

About the role

The NTI-NTU Corporate Laboratory is seeking a Research Associate to develop and apply time‑series forecasting methods for semiconductor equipment health monitoring. The role involves analysing equipment degradation data, building predictive models, and communicating insights to engineering teams.

Key responsibilities

• Develop statistical, machine learning and deep learning models for equipment health forecasting.
• Analyse degradation time‑series data to support predictive maintenance studies.
• Perform data preprocessing, signal smoothing and noise analysis on real‑world equipment datasets.
• Evaluate model performance using appropriate forecasting metrics and visualisations.
• Interpret results, identify data limitations and document findings for researchers and engineers.

Required profile

• Master's degree in Materials Science, Electrical/Electronic Engineering, Physics or a closely related field.
• Strong interest or experience in data analysis and time‑series modelling.
• Ability to work with noisy, real‑world datasets and critically interpret results.
• Good analytical thinking and clear technical communication skills.
• Post‑graduate experience in semiconductor‑related research is an advantage.

Required skills

• Python programming for data processing and model development.
• Statistical modelling techniques.
• Machine learning and deep learning methods.
• Time‑series forecasting.
• Data preprocessing, signal smoothing and noise analysis.