Research Associate (Engineering/Electrical & Material)
LocationNTU Main Campus, Singapore
Work modeon-site
Typefull-time
DepartmentResearch
Company size10,000+ people
First seen1w ago
Last seen1d ago
About the company
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 focuses on analyzing equipment degradation time-series data and developing forecasting workflows using statistical, machine learning, and deep learning models.
Key Responsibilities:
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Develop and apply time-series forecasting methods for semiconductor equipment health monitoring.
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Analyze equipment degradation data to support predictive maintenance and reliability studies.
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Implement and compare statistical, machine learning, and deep learning forecasting models.
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Perform data preprocessing, signal smoothing, and noise analysis on real-world equipment data.
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Evaluate model performance using appropriate forecasting metrics and visual analysis.
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Interpret results to determine when forecasting is meaningful and when data limitations apply.
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Document findings and communicate insights to researchers and engineering teams.
Job Requirements:
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Masters in Materials Science, Electrical Electronic Engineering, Physics, or a closely related field.
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Strong interest or experience in data analysis and time-series modeling.
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Proficiency in Python for data processing and model development.
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Basic knowledge of machine learning or deep learning techniques.
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Ability to work with noisy, real-world datasets and interpret results critically.
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Good analytical thinking and clear technical communication skills.
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Postgraduate degree or experience in semiconductor-related research is an advantage.
Hiring process
We regret that only shortlisted candidates will be notified.