Research Fellow in Maritime Line Optimization
National University of Singapore
- Singapore
- Permanent
- Full-time
- Conduct extensive research to understand and improve the complex systems of liner shipping service networks, with a particular focus on service network structure, scheduling, and fleet management.
- Utilize machine learning techniques, particularly supervised learning and reinforcement learning, to predict maritime service demand and optimize line designs.
- Implement the Monte Carlo Tree Search (MCTS) methodology to explore and evaluate optimal line configurations, contributing to the dynamic and cost-effective operation of maritime networks.
- Collaborate with a team to develop and refine methodologies, and translate findings into actionable optimization strategies for immediate and incremental improvements.
- Produce scholarly articles for journal publication, present findings at relevant conferences, and develop educational materials based on the research.
- PhD in Operations Research, Transportation Engineering, Industrial Engineering, or related fields.
- Experience in maritime industry research, particularly in liner shipping service network optimization.
- Strong background in machine learning, especially in supervised learning and reinforcement learning.
- Excellent analytical, problem-solving, and computational skills.
- Ability to work collaboratively in a multidisciplinary team and communicate complex ideas effectively.
- Track record of scholarly publications in related fields is an advantage.