
Search by job, company or skills

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.
NUS Career Portal link: https://careers.nus.edu.sg/job/Postdoctoral-Research-Fellow-%28Operations-Research-&-Data-Driven-Logistics-Optimization%29/32697-en_GB/st=0E7F9982251B8187665DF1C1E26DE55694065A4E
We regret that only shortlisted candidates will be notified.
The postdoctoral researcher will work on a research project related to improving haulier operational efficiency through mathematical modeling and data-driven decision making. The project focuses on designing effective incentive and information-sharing mechanisms for depots, hauliers, and related logistics stakeholders. The researcher is expected to develop analytical models, conduct data analysis, and generate managerial insights on how depot-level decisions, operational coordination, and information transparency can improve resource utilization, reduce congestion and delays, and enhance overall logistics efficiency. The role will involve close collaboration with the principal investigator and industry or institutional partners, contributing to model development, empirical or computational analysis, academic paper writing, and research dissemination.
The ideal candidate should have a strong background in operations management, operations research, industrial engineering, transportation/logistics, or a related field. Strong analytical skills in mathematical modeling, optimization, game theory, stochastic modeling, or mechanism design are highly desirable. The candidate should also be comfortable with data-driven decision making, including data cleaning, statistical analysis, and computational implementation using tools such as Python, R, MATLAB, or similar platforms. Experience with logistics, freight transportation, depot or terminal operations, platform operations, or supply chain coordination would be a strong advantage. The candidate should have good academic writing skills, the ability to work independently, and the motivation to translate rigorous research into practical insights for improving haulier and depot operations.
Job ID: 146611439