Research Associate (Engineering/Electrical & Material)

Nanyang Technological University Singapore; Singapore; On-site; SGD 40,000 - 70,000; Full time

Job summary

Nanyang Technological University Singapore is seeking a Research Associate for their NTI-NTU Corporate Laboratory. The role focuses on analyzing equipment degradation data and developing forecasting workflows using statistical, machine learning, and deep learning models. Applicants require a Master's degree in Materials Science or a related field and proficiency in Python programming. The position offers a chance to work with real-world datasets and contribute to innovative technologies in semiconductor research.

Qualifications

• Masters in a relevant field such as Materials Science, Electrical Electronic Engineering, or Physics.
• Proficient in Python for model development and data processing.
• Experience with noisy, real-world datasets.

Responsibilities

• Develop time-series forecasting methods for equipment monitoring.
• Analyze degradation data for predictive maintenance.
• Implement and compare various forecasting models.

Skills

Data analysis

Time-series modeling

Python programming

Machine learning techniques

Analytical thinking

Education

Masters in Materials Science or related field

Job description

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 analysing 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.