Research Fellow, Machine Learning, Vessel Collision Avoidance System

01 Mar 2023
End of advertisement period
31 Mar 2023
Contract Type
Full Time

Job Description

Vessel Collision Avoidance System (VCAS) is a real-time framework that has been developed to predict and prevent vessel collisions based on historical movement of vessels in heavy traffic regions such as Singapore Strait. 

We are looking for talented individuals to join our expanding team to help us further develop machine learning algorithms and agent-based simulation models to quantify vessel collision risk at Singapore strait and port.

If you are motivated by solving a real-world problems, connect with us to find out more how you can be part of this project.

Interested candidates should upload their detailed curriculum vitae giving full details of research experience and list of publications.


  • A PhD in a quantitative field (e.g., Computer Science, Statistics, Engineering, Science).
  • Excellent coding skills to able to solve problems in a fast pace. Familiar with popular machine learning models and eager to learn new things.
  • Ability to work as a team and possess initiatives to assume responsibilities.
  • Must be result oriented and meet the deliverables with focus on driving impact.

Covid 19 Message

At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.

Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.

More Information

Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : Industrial Systems Engineering and Management
Employee Referral Eligible: No
Job requisition ID : 11288

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