Job Description

The ECE Department at NUS is seeking a Research Fellow to support applied AI research in failure analysis and fault isolation. The successful candidate will focus on developing and integrating machine learning–guided failure analysis and diagnostic capabilities for hybrid electronics. This position offers an exciting opportunity to work at the forefront of semiconductor technology, with close collaboration across industry partners and academic researchers.

Responsibilities will involve:
1.    Research & Development
       a. Conduct research on AI/ML techniques for semiconductor failure analysis.
       b. Develop novel algorithms and methodologies for defect detection, classification, and failure analysis.
       c. Supervise and mentor junior researchers and graduate students in related research activities.

2.    System Integration
       a. Lead the design and integration of ML-driven diagnostic tools into failure analysis workflows.
       b. Build robust and scalable software solutions interfacing with diagnostic instruments and test platforms.
       c. Drive optimization of system performance for real-world deployment.

3.    Data Analysis & Reporting
       a. Manage the collection, preprocessing, and large-scale analysis of experimental and simulation datasets.
       b. Validate and benchmark AI-driven predictions against empirical outcomes.
       c. Prepare technical reports, publications, and presentations for both academic and industry audiences.

4.    Project Management & Collaboration
       a. Take responsibility for delivering research milestones within project timelines.
       b. Serve as the primary liaison with industry partners to align objectives and ensure impactful outcomes.

5.    Innovation & Intellectual Property
       a. Drive innovation in diagnostic methodologies and failure analysis workflows.
       b. Contribute to patent filings, technology disclosures, and commercialization efforts.
       c. Publish findings in high-impact journals and present at leading conferences.

Qualifications

•    Ph.D. degree in Electrical, Electronics, Mechanical, Chemical Engineering, Physics, Material Science or its equivalent from a reputable University/Institute is preferred, or equivalent related experience. 
•    Candidate with experience in microelectronic research, characterisation, metrology and electrical testing, and strong in data analysis will have advantage.
•    Proficiency in data analysis and interpretation using statistical tools and software packages.
•    Excellent communication skills and the ability to work effectively in a collaborative research environment.
•    Motivated and possesses effective communication skills including writing and presenting.
•    Ability to work independently and collaborate within a diverse team.
•    Open to Fixed Term Contract.

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : Electrical and Computer Engineering

Employee Referral Eligible: No

Job requisition ID : 30454