Job Description

The successful candidate will work with Asst.  Prof. Julian Sester on developing mathematical methods for Distributionally Robust Reinforcement Learning.

The main responsibilities of the position include:
•    Developing rigorous mathematical foundations for distributionally robust reinforcement learning under model uncertainty; 
•    Studying robust Markov decision processes, dynamic programming principles, and convergence properties of robust learning algorithms; 
•    Designing and analysing reinforcement learning algorithms based on Wasserstein, Sinkhorn, or related ambiguity sets; 
•    Establishing theoretical guarantees such as stability, convergence, approximation error bounds, or sensitivity estimates; 
•    Implementing and testing proposed methods in numerical case studies, with possible applications in quantitative finance, stochastic control, or risk management; 
•    Preparing research manuscripts for publication in leading journals and presenting results at seminars, workshops, and conferences; 
•    Contributing to the research environment of the group, including discussions with PhD students, postdoctoral researchers, and collaborators.

Qualifications / Discipline:

•    PhD in Mathematics, Applied Mathematics, Statistics, Operations Research, Quantitative Finance, or a closely related discipline.

Skills:
•    Strong background in probability theory, stochastic processes, stochastic control, optimization, or reinforcement learning; 
•    Solid mathematical training and ability to work with rigorous proofs; 
•    Familiarity with Markov decision processes, dynamic programming, distributionally robust optimization, optimal transport, or reinforcement learning is highly desirable; 
•    Programming skills in Python are desirable, especially experience with numerical experiments, machine learning libraries, or reinforcement learning environments; 
•    Good written and oral communication skills; 
•    Ability to work independently and collaboratively in an interdisciplinary research environment. 

Experience:
•    Prior research experience in one or more of the following areas is desirable: reinforcement learning, robust control, stochastic control, distributionally robust optimization, optimal transport, mathematical finance, or machine learning; 
•    A track record of high-quality research, demonstrated through publications, preprints, or a strong PhD thesis; 
•    Experience with numerical implementation of mathematical or machine learning methods would be an advantage; 
•    Experience in quantitative finance or financial applications is welcome but not required.