Research Assistant: Object Detection & Few-Shot Learning

ntu; Singapore; On-site; SGD 40,000 - 54,000; Full time

Job summary

NTU Singapore invites applications for a Research Assistant to join a university‑level research centre collaborating with industry on object detection.

You will conduct lab work, support data‑efficient object detection research, and prepare technical reports and publications for dissemination to the academic community. The ideal candidate has a science or engineering background and a proactive, collaborative mindset.

Qualifications

• Bachelor's degree in Computer Science, Electrical/Computer Engineering, or related field.
• Basic understanding of machine learning and computer vision.
• Familiarity with Python and at least one deep learning framework (e.g., PyTorch, TensorFlow, or Keras).
• Interest in research and willingness to learn and explore new concepts.
• Good communication skills and ability to work both independently and as part of a team.
• Strong motivation, initiative, and a passion for solving technical challenges.

Responsibilities

• Participate in lab work and support on data-efficient object detection with key techniques in few-shot learning, transfer learning, image synthesis, etc.
• Assist in preparing technical documentation, reports, and research reports and/or publications as required by the funding body or for dissemination to the wider academic community.
• To undertake any other duties relevant to the programme of research.

Skills

Machine learning basics

Computer vision

Communication skills

Independent working

Initiative

Team collaboration

Education

Bachelor's degree in Computer Science/Engineering or related

Tools

Python

PyTorch

TensorFlow

Keras

Job description

NTU Singapore invites applications for a Research Assistant to join a university‑level research centre collaborating with industry on object detection.

You will conduct lab work, support data‑efficient object detection research, and prepare technical reports and publications for dissemination to the academic community. The ideal candidate has a science or engineering background and a proactive, collaborative mindset.