We are looking for a highly motivated, positive and hardworking PhD candidate to work within the project Combinatorial Optimization with Uncertainty.
Combinatorial optimization problems are ubiquitous in many domains such as logistics, production, health care and computer processing.
Classical approaches often assume that an algorithm is all-knowing, while in reality parameters of a problem are often unknown or uncertain.
Dealing with this uncertainty is a major challenge in combinatorial optimization.
The primary interest for this project is online combinatorial optimization where parameters are revealed to the algorithm over time.
These models, however, tend to result in very pessimistic performance bounds. Therefore, we consider models that mitigate this tendency.
Particularly, models that combine classical online combinatorial optimization problems with models known from optimal stopping theory, like the random order model (well-known from the secretary problem) or the stochastic information model (well-known from the prophet inequality).
The aim is to develop and analyze new algorithms that deal with uncertainty in combinatorial optimization problems in these information models.
In consultation with your supervisor you will have a lot of freedom to decide the direction of the project.
You will be supervised by Ruben Hoeksma and will be part of the DMMP group headed by Marc Uetz. For further information about the group, click here
The terms of employment are in accordance with the Dutch Collective Labour Agreement for Universities (CAO) and include :