The research fellow will be conducting research for a project on facial image processing and understanding, extending and pushing the frontiers on deep learning-based methods.
The potential topics that the researcher may be assigned to work on include:
- Accurate dynamic 3D reconstruction of facial shape, appearance and motion,
- Inference of underlying personal attributes including but not limited to gender, age, emotions and moods, and;
- Output image/video manipulation such as video-driven facial animation of avatars and caricatures, face relighting and facial attribute transfer between different individuals.
The research fellow is expected demonstrate good leadership in leading a research team to come up with independent research ideas. The RF is expected to both conduct and coordinate extensive software coding on the latest research platforms, build and showcase research demo systems, as well as conduct data collection and processing. The RF must be able to lead her or his team in coordinating and writing research papers. The RF should have high integrity, be self-motivated, independent and proactive, have matured reasoning and planning skills, and be able to lead and establish high morale teams.
- The candidate is expected to have a good doctorate degree in computer science or related discipline, with strong mathematical ability and extensive experience programming in C/C++ as well as Python on the latest deep learning platforms, and familiarity with LaTeX.
- The candidate must be able to communicate well in English, with good presentation skills.
- The candidate should also have a good publication track record, especially in top-tier conferences and/or journals.
Interested applicants please attach your full CV (including educational background, research and work experience(s), list of publications and highlighting previous relevant experience), the names and contacts (including email addresses) of 3 character referees, and all relevant academic certificates to Assoc Prof Cham Tat Jen (ASTJCham@ntu.edu.sg).
We regret that only shortlisted candidates will be notified.
Applications close when position is filled.