Applications are invited for a two-year postdoctoral research position at NUS, Singapore, with no teaching duties. The successful candidate will work with Julian Sester and his research group.

The successful candidate will have a PhD and an outstanding research profile in Mathematics, Applied Mathematics, Statistics, Operations Research, Quantitative Finance, or a closely related field. A competitive salary will be provided.

The project focuses on rigorous mathematical foundations and algorithms for reinforcement learning under model uncertainty. Topics include Distributionally Robust Reinforcement Learning, Model Uncertainty in Finance, and X-Optimal Transport, as well as robust Markov decision processes, dynamic programming, Wasserstein/Sinkhorn ambiguity sets, convergence analysis, stability, approximation error bounds, and numerical implementation.

The candidate will be expected to conduct theoretical and computational research, prepare manuscripts for leading journals, present results at seminars and conference, and actively contribute to the research and outreach activities of the group.

A strong background in probability, stochastic processes, stochastic control, optimization, or reinforcement learning is expected. Experience with Markov decision processes, distributionally robust optimization, optimal transport, robust control, mathematical finance, or machine learning is highly desirable. Programming experience in Python and numerical implementation would be an advantage.

Application materials:
Applicants should send a CV and 1–2 letters of recommendation. Review of applications begins immediately and continues until the position is filled. Short-listed candidates may be invited for an interview.

All files should be emailed as PDF attachments to: jul_ses@nus.edu.sg.