Research Fellow (Operations Research & Analytics)
University-Level Unit: Inst of Operations Research & Analytics
Faculty/Department-Level Unit: Inst of Operations Research & Analytics
Employee Category: Research Staff
Location: Kent Ridge Campus
Posting Start Date: 11/05/2026
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.