Research Associate - Time-Series Forecasting for Equipment

Nanyang Technological University; Singapore; On-site; SGD 45,000 - 65,000; Full time

Job summary

Nanyang Technological University’s NTI-NTU Corporate Laboratory invites applications for a Research Associate to analyze equipment degradation time-series data and develop forecasting workflows using statistical, machine learning, and deep learning models.

You will work with researchers and engineers to preprocess data, apply forecasting methods, evaluate performance, and communicate insights that support predictive maintenance and reliability studies.

Qualifications

• Masters in Materials Science, Electrical/Electronic Engineering, Physics, or related field.
• Strong interest or experience in data analysis and time-series modeling.
• Proficiency in Python for data processing and model development.
• Knowledge of machine learning or deep learning techniques is a plus.

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 forecasting metrics and visual analysis.

Skills

Time-series analysis

Data analysis

Python

Forecasting

Education

Masters in Materials Science, Electrical/Electronic Engineering, Physics, or related field

Tools

Python

Job description

Nanyang Technological University’s NTI-NTU Corporate Laboratory invites applications for a Research Associate to analyze equipment degradation time-series data and develop forecasting workflows using statistical, machine learning, and deep learning models.

You will work with researchers and engineers to preprocess data, apply forecasting methods, evaluate performance, and communicate insights that support predictive maintenance and reliability studies.