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Research Associate (Object Detection/ Segmentation)

NtuWorkdayNTU Main Campus, Singapore
Posted 10 SepFound 27 OctSeen 5 hours ago

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The Posting

Hyundai-NTU-A*STAR Corp Lab has a robotic pick and place project. We are seeking to hire a research associate to carry the computer vision part of the project.

Key Responsibilities:

  • Develop 3D objects and segmentation models for a robotic pick and place project.
  • Develop data augmentation/data synthesis methods to address challenges from limited training data.
  • Develop algorithms to annotate 2D images through 3D images.
  • Check data quality.
  • Review and identify the weaknesses of existing methods for 3D object detection and segmentation.
  • Carry out experiments and analyze the results.
  • Develop a robotic pick and place with other researchers and engineers.
  • Prepare reports and scientific papers.

Job Requirements:

  • Master’s in Computer Engineering, Computer Science, Electronics Engineering or equivalent.
  • More than 2 years of research experience preferred.
  • In-depth knowledge of deep learning, specifically, 2D and 3D object detection and segmentation, SAM, foundation models, diffusion models, transfer learning and few-shot object detection and segmentation, 3D computer vision.
  • Preferably with publications at top AI conferences, such as CVPR, ICCV, ECCV, NeurIPS, ICML and ICRL.
  • Strong research capabilities, including innovative thinking, hypothesis formulation, experimental design, and project reporting.
  • Collaborative and communication skills, with a proactive, analytical mindset and ability to work independently or as part of a team.
  • Preferred technical expertise in software implementation and knowledge of robotics.

We regret that only shortlisted candidates will be notified.

Hiring Institution: NTU

Facts

Source
Workday
External id
R00021865-1
Location
NTU Main Campus, Singapore
Remote
Not stated
Employment
Full time
Posted
10 Sep 2025
First seen
27 Oct 2026, 10:51
Last seen
2 Nov 2026, 22:05
Status
Not yet added
Posting id
6c1b0911-a9e9-58c5-8851-2aa35062ad07