Research Associate (Engineering & Electrical & Material)

NANYANG TECHNOLOGICAL UNIVERSITY; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading educational institution in Singapore is seeking a Research Associate to analyze equipment degradation time-series data and develop forecasting workflows using statistical and machine learning models. The ideal candidate will have a Masters in a relevant field and strong skills in data analysis and Python. Key responsibilities include developing forecasting methods and implementing machine learning techniques to enhance predictive maintenance practices. Only shortlisted candidates will be notified.

Qualifications

• Masters degree in a relevant field.
• Strong interest or experience in data analysis.
• Proficiency in Python for model development.

Responsibilities

• Develop time-series forecasting methods.
• Analyze equipment degradation data for predictive maintenance.
• Implement statistical, machine learning, and deep learning models.

Skills

Data analysis

Time-series modeling

Python

Machine learning techniques

Analytical thinking

Technical communication

Education

Masters in Materials Science, Electrical Electronic Engineering, Physics

Job description

NTI-NTU Corporate Laboratory

NTI-NTU Corporate Laboratory is a collaboration between Nanofilm Technologies International Limited (“Nanofilm”, “NTI”), Nanyang Technological University (“NTU”) and supported by Singapore under RIE2030. The Laboratory’s objective is to propel Innovation and Technologies commercialisation through NTU’s innovation and NTI’s deep technology. NTI-NTU Corporate Laboratory aligns with Singapore’s RIE2030 handbook – which emphasises the nation’s commitment to research and innovation, aiming to drive economic growth and address national challenges.

The Nanyang Technological University NTI-NTU Corporate Laboratory is seeking to hire a Research Associate. The selected candidate will focus on analyzing equipment degradation time-series data and developing forecasting workflows using statistical, machine learning, and deep learning models.

Key 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.
• Document findings and communicate insights to researchers and engineering teams.

Job Requirements

• Masters in Materials Science, Electrical Electronic Engineering, Physics, or a closely related field.
• Strong interest or experience in data analysis and time-series modeling.
• Proficiency in Python for data processing and model development.
• Basic knowledge of machine learning or deep learning techniques.
• Ability to work with noisy, real-world datasets and interpret results critically.
• Good analytical thinking and clear technical communication skills.
• Postgraduate degree or experience in semiconductor-related research is an advantage.

We regret that only shortlisted candidates will be notified.