Research Fellow - Computer Vision and Machine Learning

About the role

Nanyang Technological University (NTU) Singapore, College of Computing and Data Science, is seeking a research fellow to join a university-level research centre collaborating with industry. The position is for one year, renewable subject to satisfactory performance, and focuses on developing 3D vision. Candidates must have a strong background in the relevant fields.

Key responsibilities

• Independently carry out research in computer vision and machine learning.
• Produce research reports and/or publications as required by the funding body or for wider academic dissemination.
• Guide and support students associated with the project.
• Attend, contribute to and, where necessary, lead relevant meetings.
• Undertake other duties relevant to the research programme.
• Assist in postgraduate student supervision, help with small-group teaching such as undergraduate labs and tutorials, and initiate collaboration research in a multidisciplinary team.
• Oversee and report project progress.

Required profile

• PhD degree or at least 8 years of working experience in Computer Engineering, Computer Science, Electronics Engineering or an equivalent field.
• Strong publication record in top-tier machine learning or computer vision venues such as TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, ICML and ICLR.
• Proven research ability shown through a portfolio of publications, conference papers and/or patents.
• Strong R&D background, with the ability to develop and manage R&D projects and understand industrial needs.
• Ability to formulate hypotheses, design experiments and implement new experimental methods, including prototype system engineering and laboratory work.
• Willingness to work some flexible hours.

Required skills

• Strong background in machine learning and computer vision; prior experience in 3D vision is preferable.
• Knowledge of scientific or engineering software such as PyTorch, TensorFlow and Keras.
• High-level expertise in the required experiment or modelling methods.