Research Associate — Computer Vision & AI Defect Detection

ntu; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

NTU is seeking a Research Associate to advance the project entitled "Manual Assembly Job Quality Inspection" by researching and developing AI algorithms for object and defect detection. The role includes system integration and budget coordination.

The candidate should hold a Master’s degree in CS/EE, have 1+ year in computer vision, and be proficient in Python/C with PyTorch and TensorFlow. Strong communication and teamwork are essential, with publications preferred.

Qualifications

• Master's degree in Computer Science, Electrical/Electronic Engineering, or related field.
• Minimum 1 year of relevant experience in computer vision, AI, etc.
• Proficiency in programming languages like C and Python, and deep learning frameworks such as PyTorch and TensorFlow.
• Knowledge in imaging and computing devices and equipment.
• Strong communication, organizational, and time management skills; able to work independently and in a team.
• Prior publication experience is preferred.

Responsibilities

• Develop AI technologies for object and defect detection.
• Integrate developed algorithms into systems.
• Oversee project budget and coordinate procurement and maintenance of equipment.
• Provide mentorship and academic guidance to students.
• Prepare and compile reports pertaining to the project.

Skills

Object detection

Research

Communication

Teamwork

Education

Master's degree in Computer Science / EEE or related field

Tools

Python

C

PyTorch

TensorFlow

Job description

NTU is seeking a Research Associate to advance the project entitled "Manual Assembly Job Quality Inspection" by researching and developing AI algorithms for object and defect detection. The role includes system integration and budget coordination.

The candidate should hold a Master’s degree in CS/EE, have 1+ year in computer vision, and be proficient in Python/C with PyTorch and TensorFlow. Strong communication and teamwork are essential, with publications preferred.