Machine learning techniques for semiconductor failure analysis
Defect detection and classification algorithms
Integration of ml-driven diagnostic tools
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The National University of Singapore (NUS) is seeking a Research Fellow to focus on developing and integrating machine learning techniques for failure analysis and fault isolation in hybrid electronics. This role involves collaboration with industry partners and academic researchers, emphasizing research and development, system integration, and project management.
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Job Summary
The role focuses on developing novel AI/ML algorithms for defect detection and failure analysis in hybrid electronics.
Candidates will lead the integration of ML-driven diagnostic tools into existing failure analysis workflows with industry partners.
The position requires managing large-scale data analysis, validating predictions against empirical outcomes, and contributing to patent filings.
Matching Summary
Match Score: 75
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The National University of Singapore (NUS) is seeking a Research Fellow to focus on developing and integrating machine learning techniques for failure analysis and fault isolation in hybrid electronics. This role involves collaboration with industry partners and academic researchers, emphasizing research and development, system integration, and project management.
**
Skills & Requirements
Must-have
Machine learning techniques for semiconductor failure analysis
Defect detection and classification algorithms
Integration of ML-driven diagnostic tools
Large-scale experimental dataset analysis
Nice-to-have
Experience in microelectronic research and characterization
Strong background in metrology and electrical testing
Ability to mentor junior researchers and students
Excellent communication and presentation skills
Key Requirements
Ph.D. in Electrical, Electronics, Mechanical, Chemical Engineering, Physics, or Material Science
Equivalent related experience in microelectronics or data analysis
Proficiency in statistical tools and software packages for data interpretation