"Research Fellow (Improving Efficiency of Container Operations with Bundling)"

Job Description

The Research Fellow will contribute to a project on Improving Efficiency of Container Operations with Bundling, motivated by Singapore’s position as a leading global transshipment hub facing growing container volumes, land constraints, and sustainability challenges. The project focuses on the use of data analytics to study bundling strategies in container operations, such as consolidating container flows, coordinating handling activities, and synchronizing schedules across vessels, terminals, and hinterland transport. By leveraging large scale operational data from port and logistics systems in Singapore, the Research Fellow will develop analytical and empirical models to evaluate how bundling decisions affect congestion, resource utilization, service reliability, and environmental performance. The role involves close collaboration with academic researchers and industry stakeholders, with the objective of generating data driven insights to support more efficient and sustainable container operations in Singapore.

Job Requirements

The candidate should hold a PhD or be close to completion in operations research, industrial engineering, data science, analytics, or a related discipline. The position requires strong analytical and quantitative skills, with experience in data analytics, statistical modeling, optimization, or machine learning applied to large scale operational data. Proficiency in programming languages such as Python, R, or MATLAB is expected, along with experience handling real world datasets. The candidate should have a solid foundation in operations management or logistics systems and an interest in container terminal or port operations. Strong communication skills are required to work effectively with academic collaborators and industry partners in Singapore, and prior experience with applied research or industry facing projects is an advantage.

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: 31421

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