Research Associate (EngineeringElectrical & Material)

Nanyang Technological University

Job Location:

Singapore - Singapore

Monthly Salary: Not Disclosed

Posted on: 3 hours ago

Vacancies: 1 Vacancy

Job Summary

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 Laboratorys objective is to propel Innovation and Technologies commercialisation through NTUs innovation and NTIs deep technology. NTI-NTU Corporate Laboratory aligns with Singapores RIE2030 handbook which emphasises the nations 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:

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.

Hiring Institution: NTU

Required Experience:

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.

Hiring Institution: NTU

Required Experience:

Key Skills

Laboratory Experience
ELISA
Immunoassays
Mammalian Cell Culture
Biochemistry
Cell Biology
Research Experience
Cell Culture
Molecular Biology
Microscopy
Research Laboratory Experience
Western Blot

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About Company

Nanyang Technological University

Nanyang Technological University is one of the top universities in Singapore offering undergraduate and postgraduate education in engineering, business, science, humanities, arts, social sciences, education and medicine.

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