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VJLG

捐助贵宾 (初入文坛)

应助: 0 (幼儿园)
金币: 48
帖子: 15
在线: 10.2小时
虫号: 8540556

[交流] 新加坡国立大学招收博士后/研究助理，方向: 建筑机器人；雷达激光扫描；语义导航

We're currently seeking candidates for Postdoc and Research Assistant positions. Our research group focuses on robotic scanning and building information modelling (BIM) technologies. We are interested in integration of LiDAR, image-based sensing, and semantic AI algorithms with mobile robotic platforms for automated 3D scanning of the built environment, followed by high-precision 3D BIM reconstruction and semantic enrichment for legacy buildings or infrastructures.

The candidates should have relevant background and knowledge in civil engineering, mechanical engineering, electrical engineering, computer engineering, or a related discipline. Interested applicants are invited to submit their CV and papers to Assistant Professor Vincent at vincent.gan@nus.edu.sg by 20 March 2026. Shortlisted candidates will be contacted for an interview.

The topic is related to robotic scanning, 3D perception, social navigation and/or last mile delivery in clutter, human-centric environments. Below is a list of expected experience for the candidate:
1. Research experience and proficiency with laser scanning, LiDAR, computer vision, and Simultaneous Localization and Mapping (SLAM), including open-source frameworks and algorithms for 3D scene reconstruction.
2. Research experience and skills in 3D perception, mapping, object detection, ground robot navigation, and collision avoidance. Knowledge in semantic map and robot trajectory optimisation, especially for indoor GPS-denied, clutter or human-centric environment, would be beneficial.
3. Strong ability in programming and familiarity with the LINUX platform. Experience in machine learning and deep learning.
4. A proven track record of peer-reviewed publications

The appointed candidates will support the planning and execution of research projects alongside postdoc, PhD students, and RAs. This includes conducting research, analysing results, preparing reports and papers. Candidates should demonstrate knowledge and expertise in the aforementioned areas, with a proven track record of peer-reviewed publications. Proficiency in written and spoken English is required.

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