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/Research-Fellow-%28Operations-Research-&-Analytics%29/32836-en_GB/

We regret that only shortlisted candidates will be notified.

About the Role

The Institute of Operations Research and Analytics is seeking a Research Fellow to conduct high-impact research at the intersection of data-driven decision making, robust control, and online learning, with a strong interest in real-world deployment and decision support. Experience in financial applications—including dynamic investment and portfolio choice problems—is desirable.

Key responsibilities

• Conduct independent and collaborative research in areas including:
• data-driven decision making and sequential decision problems
• robust / distributionally robust optimization and robust control
• online learning, bandits, and reinforcement learning for operations/finance
• Develop theoretical models and algorithms, and validate them via simulation and/or real datasets.
• Apply methodologies to financial decision-making problems (e.g., dynamic asset allocation, portfolio optimization, risk-sensitive control), where relevant.
• Publish in top-tier journals and conferences; contribute to research proposals and reports.
• Collaborate with faculty, research staff, and external stakeholders/industry partners.
• Support mentoring of graduate students and contribute to the Institute’s research activities (seminars, reading groups, workshops).

Requirements

• PhD (completed or near completion) in Operations Research, Industrial Engineering, Computer Science, Statistics, Applied Mathematics, Electrical Engineering, or a related field.
• Demonstrated research capability in one or more of the following:
• data-driven optimization / decision making
• robust control / stochastic control
• online learning / bandits / reinforcement learning
• Strong foundations in optimization, probability, and statistical learning.
• Solid programming skills (e.g., Python/Julia/Matlab; experience with ML/optimization libraries is a plus).
• Track record (or strong potential) for high-quality publications and collaborative research.