Job description

As a PhD-TA candidate, you will work on new distributed algorithms for large networks, in particular, computing the stationary distribution of random walks and policy evaluation. Random walks are used in network analysis for ranking vertices, community detection, and embedding of the vertices in multi-dimensional space for machine learning tasks. Policy evaluation is widely used in reinforcement learning, for instance, for training large language models. Your challenge is to develop, analyze and implement algorithms where the network vertices act independently by learning from their local observations. You will mathematically analyze the algorithms on random graph models and implement them on large real-life networks.

The position is closely related to the NETWORKS program of the SPOR cluster at the TU/e. You will join a lively group of academic staff, PhD candidates and postdocs who all share their fascination for mathematics of complex networks.  You will greatly benefit from the many workshops on networks and random graphs at the Eurandom conference center based at the Department of Mathematics and Computer Science at the TU/e.

The PhD TA position is for 5 years with 25% of your time spent on education. Your advisor, as well as other members of the Probability group, are committed to effective innovative education. You will have the opportunity to use your creativity for designing, guiding and assessing new inspiring learning activities for the students, while we will guide you in your growth as educator.

Job requirements

• A master’s degree (or an equivalent university degree) in mathematics or applied mathematics.
• A research-oriented attitude.
• Motivated to develop your teaching skills and coach students.
• Motivated to develop and implement innovative course design
• Fluent in spoken and written English (C1 level).