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.