Postdoctoral Research Fellow (Collaborative Logistics Optimization & Fairness)

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-%28Collaborative-Logistics-Optimization-&-Fairness%29/32691-en_GB/st=0D823E49D0C6E8B8015C5538CE92A49F95E30B38

We regret that only shortlisted candidates will be notified.

Job Description

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 cooperation designs amongst Transport companies and design job sharing mechanisms to improve efficiency and maintain fairness. The researcher is expected to develop analytical models, conduct data analysis, design simulation scenarios and generate managerial insights on the effect of the cooperation models on the 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.

Skills needed

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, and programming skills 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.