Sea Industrial PhD Program

An Initiative for the Best and the Brightest Global Talents (Open to All Nationalities)

Nurturing talent is one of SAIL’s core missions. With support from the Singapore Economic Development Board and the National University of Singapore (NUS), we are pleased to offer the Sea Industrial Postgraduate Programme (IPP) to promising individuals from all backgrounds who wish to pursue a relevant postgraduate degree whilst concurrently contributing to cutting-edge research at SAIL.

The Sea IPP gives prospective candidates the best of both worlds - a challenging and rewarding research career at SAIL as well as the opportunity to pursue world-class postgraduate qualifications at NUS. Sea IPP trainees undertake a jointly-identified research project supervised by both NUS and SAIL that contributes to their degree requirements.

Eligibility Criteria

The Sea IPP is open to global applicants who meet the following criteria:

  • Possess a strong interest in a career in computer science;
  • Must meet NUS's postgraduate admission and academic requirements;
  • Obtained Bachelor's or Master's degree in Computer Science/Engineering, Electrical/Electronic, Mathematics, Physics, Medicine/Life Sciences or related areas (Preferred, but not limited to);
  • Keen to study and perform research in the areas of Artificial Intelligence, Computer Vision, Data Mining, Deep Learning, Game AI, Genomic Computing, Machine Learning, Natural Language Processing, Reinforcement Learning, Speech Recognition (Preferred but not limited to);
  • Not a recipient of, or hold any other scholarship or other award of a monetary nature (Excluding bursary and financial aid);
  • Open to all nationalities, with Singapore Citizens and Permanent Residents strongly encouraged to apply; and
  • For existing PhD candidates, must be 2 years or less into the NUS postgraduate programs.

Benefits

Sea IPP trainees will be able to enjoy the following benefits:

  • Competitive monthly salary including many employment benefits
  • Central Provident Fund contributions (for Singapore citizens and permanent residents only)
  • Medical Insurance, visa fee, etc.
  • Sponsorship of tuition fee and other university fees for 4 years
  • Conference and travel allowances
  • Computing equipment and resources

Application Process

To kickstart the Sea IPP application process, please complete the following steps:

Step 1: Submit online application in NUS Graduate Admissions System. You are required to prepare your supporting documents for uploading to the online system. (Refer to the SoC website for the latest supporting documents required by NUS.)

Step 2: Once submitted in the NUS Graduate Admissions System, please notify our Recruiter Lead, Kayla (kaylajs@sea.com) with quoting your NUS Application Number and the following supporting documents:

  • Personal Statement
  • Curriculum Vitae
  • Proof of Residence
  • Education Certificates
  • Test Scores: GRE(General)/GMAT and TOEFL/IELTS (if medium of instruction at university is not English)
  • 2 Referee's Reports/Recommendations
  • Other Supporting Documents (Such as copy of publications, etc)

Step 3: Once submitted, we will contact the shortlisted candidates for SAIL interviews, in conjunction with the NUS PhD interview process. Both processes take approximately four to six weeks to complete, including document authenticity verification.

Step 4: Applicants may receive their application outcome by SAIL and NUS two months from the application deadline. An offer package by NUS will be sent via email to the successful candidates at a later date. Applicants who did not receive their outcome status may contact Kayla (kaylajs@sea.com).

Application Deadline

Our Sea-IPP applications are accepted throughout the year, and will be considered for the next nearest intake, following the NUS academic calendar (August and January intakes), subject to first-come-first-served basis. The cut-off dates for each intake are as follows:

  • January 2023 Intake: 15 September 2022
  • August 2023 Intake: 5 January 2023

For local applicants, please refer to the alternative deadlines below. However, local applicants are advised to submit applications as early as possible since scholarship places will be allocated as soon as qualified candidates are shortlisted.

  • January 2023 Intake: 15 September 2022
  • August 2023 Intake: 15 March 2023

Other Information

If you have any queries regarding our Sea IPP, please contact Kayla (kaylajs@sea.com).

Latest Publications

Inception Transformer
Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan
We present a novel and general-purpose Inception Transformer, or iFormer for short, that effectively learns comprehensive features with both high- and low-frequency information in visual data for tackling Transformer is incompetent in capturing high frequencies that predominantly convey local information.
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan
The trade-off between robustness and accuracy has been widely studied in the adversarial literature. Although still controversial, the prevailing view is that this trade-off is inherent, either empirically or theoretically. Thus, we dig for the origin of this trade-off in adversarial training and find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance -- an overcorrection towards smoothness. Given this, we advocate employing local equivariance to describe the ideal behavior of a robust model, leading to a self-consistent robust error named SCORE. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty via robust optimization. By simply substituting KL divergence with variants of distance metrics, SCORE can be efficiently minimized. Empirically, our models achieve top-rank performance on RobustBench under AutoAttack. Besides, SCORE provides instructive insights for explaining the overfitting phenomenon and semantic input gradients observed on robust models.

We are hiring!