AICS PhD Trainee Program (Singapore)
AICS PhD Trainee Program - Singapore Office
[ The application is now closed ]
The PhD trainee will enrol in a PhD programme with one of our local partner universities under the Industrial Postgraduate Programme (IPP) and conduct ambitious long-term research under the joint supervision of a faculty member and an AICS research staff. Specific research topics of interest include knowledge discovery, domain generalization, long-tailed learning, self-supervised approaches in NLP or CV. We look forward to hearing from you!
As an IPP PhD trainee, the candidate will receive:
- A monthly stipend of S$7,000 for up to 4 years
- Full sponsorship of tuition fees for up to 4 years
- Assigned mentors during the PhD programme
- Networking with like-minded peers, experienced PhD researchers, and leaders
- Industry exposure
- Opportunity to rotate across various technology verticals
- Career conversations and personal development check-ins
- Opportunity to embark on an R&D career at ASUS after graduation
Job Responsibilities:
- Research innovative machine learning algorithms and tools to address large-scale, real-world problems in the domains of natural language processing and/or computer vision
- Develop novel state-of-the-art methods for domain generalization, long-tailed learning, knowledge discovery
- Publish and present your work in the top AI/ML venues
- Work with our research staff and university partners, supervise students and interns
- Work with the product and engineering team to transform research output into prototypes
Requirements:
- Singaporean Citizen or Permanent Resident at the time of application
- Bachelor or Master degree in Computer Science or related discipline with excellent academic credentials
- Apply and gain admission into the PhD program with one of our partner universities (NUS, NTU, SMU, SUTD)
- Passion for tackling challenging research problems
- Strong analytical and problem-solving skills
- Some experience with deep learning frameworks such as PyTorch, Keras, or Tensorflow