Research Fellow, Machine Learning, Vessel Collision Avoidance System
5 days left
- Full Time
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.
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : Industrial Systems Engineering and Management
Employee Referral Eligible: No
Job requisition ID : 11288