Nanyang Technological University

Research Fellow (Object Detection, Recognition, Machine Learning)


The Rapid-Rich Object SEarch (ROSE) Lab is a joint research initiative between the Nanyang Technological University (NTU) and Peking University (PKU).  It aims to create a platform for fast mobile searches on object databases.  The three key thrusts of the ROSE Lab are:  (i) large-scale object database collection and analytics, (ii) scalable mobile object search with contextual mobility, and (iii) media cloud platform.  Resources at the ROSE Lab consist of NTU & PKU Faculty, Researchers, PhD Students, and Visiting Scholars & Students from other institutions from around the world. 

The successful applicant will be responsible for the development of large scale Object Detection & Recognition systems using machine learning techniques.  This includes:

(i) Building Object Detection & Recognition systems, including parallelising and optimising the algorithms developed by other ROSE Lab researchers

(ii) Building Document Classification & Information Extraction systems based on text detection & localisation techniques

(iii) Working with industry partners of the ROSE Lab to test the classification systems.

The successful applicant will typically be on a 1 year contract basis, with the option to renew based on the candidate’s performance. 

Learn more about ROSE Lab at


•  PhD degree in Engineering, Computer Science, and/or Information Systems.
•  Must have research experience in object detection & recognition in natural images, in particular the multi-oriented detection & localization of text in natural scenes
•  Experienced in C/C++ and MatLab
•  Familiar with CUDA programming and Linux.
•  Must have research experience in image processing and image classification.
•  Experience with people detection, re-identification, and tracking would be a plus.
•  Good interpersonal skills, with the ability to work with people from varied backgrounds. 

Application procedure:

Interested applicants please attach your full CV, with the names and contacts (including email addresses) of 3 character referees, and all relevant academic certificates to

We regret that only shortlisted candidates will be notified.

Application closes when the positions are filled.

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