AICS PhD Trainee Program (Singapore)

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) for the January 2023 intake 
  • Passion for tackling challenging research problems 
  • Strong analytical and problem-solving skills 
  • Some experience with deep learning frameworks such as PyTorch, Keras, or Tensorflow