"Research Assistant (Microelectronics)"

Job Description

The Electrical and Computer Engineering (ECE) Department at the National University of Singapore (NUS) is seeking qualified applicants for the position of Research Assistant to support an advanced research project on physics-informed localisation of thermal hotspot imaging in microelectronics. This position offers the opportunity to work closely with researchers, graduate students, and professors on cutting-edge methods that integrate AI, chip design, and thermal physics for defect localisation in semiconductor devices.

Responsibilities:

• Collaborate with the research team to analyse experimental and simulated thermal hotspot data.
• Design and implement AI/ML models to reverse-engineer defect locations from thermal hotspot images.
• Incorporate chip architecture and layout inputs into the model for enhanced accuracy.
• Develop back-calculation methods to account for heat dispersion across multiple layers and materials.
• Perform Pareto analysis of parameters (e.g., number of layers, material properties, depth of defect) for model optimisation.
• Work on creating physics-informed generative models that integrate theoretical hotspot dispersion mechanisms with data-driven approaches.
• Assist in preparing research reports, presentations, and publications.

We offer a competitive salary commensurate with qualifications and experience, along with a stimulating research environment and access to state-of-the-art facilities. This is a full-time position with an initial contract duration of 12 months, with potential for renewal based on performance and funding availability.

Review of applications will begin immediately and continue until the position is filled.

Qualifications

Candidate should meet at least 4 criteria from the following list:

• Bachelor’s degree in electrical engineering, Physics, Computer Science, Materials Science, or related fields.
• Strong background in at least one of the following:
  • Semiconductor device physics and BEOL processes
  • AI/ML methods (especially generative models)
  • Thermal analysis and modelling
• Experience with Python, MATLAB, or similar programming languages.
• Familiarity with CAD tools, chip architecture, or layout analysis is a plus.
• Good communication skills, with the ability to work collaboratively in a multidisciplinary team.

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department: Electrical and Computer Engineering

Employee Referral Eligible: No

Job requisition ID: 30976

Apply now