Job Description

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Job Title: Research Engineer in Maritime Line Optimization (IORA)
Posting Start Date: 25/08/2025
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The Institute of Operations Research and Analytics (IORA) at National University of Singapore (NUS) is seeking a dedicated and analytical Research Engineer to join our team. The selected candidate will engage in cutting-edge research with our industry partner, focused on optimizing the liner shipping service network, which is pivotal to global trade. This involves enhancing the efficiency of operational maritime lines through strategic modifications, addressing challenges such as line configurations, port schedules, and operational costs.
Key Responsibilities

• Develop and implement advanced optimization models for liner shipping service networks, focusing on network design, scheduling, and fleet deployment.

• Apply machine learning methods, particularly supervised learning and reinforcement learning, to forecast maritime service demand and optimize line structures.

• Design and code Monte Carlo Tree Search (MCTS) algorithms to evaluate and improve alternative service network configurations, enabling more adaptive and cost-effective operations.

• Collaborate with researchers and industry partners to refine methodologies and translate findings into actionable strategies for real-world deployment.

• Contribute to knowledge creation through technical reports, publications, and conference presentations.

• Bachelor's or Master's degree in Operations Research, Transportation Engineering, Industrial Engineering, Computer Science, or a related field.

• Strong programming skills in Python, with proven experience implementing optimization algorithms, reinforcement learning frameworks, and/or tree search methods.

• Background in transportation, logistics, or maritime networks is an advantage.

• Excellent analytical and problem-solving skills, with the ability to translate models into practical applications.

• Ability to work effectively in interdisciplinary teams and communicate technical concepts clearly.