PhD position in numerical analysis for high-dimensional function approximation

There are a lot of algorithms to approximate high-dimensional functions. They often are based on samples. We consider the case in which the sample nodes can be chosen and are interested in worst-case errors of e.g. the L2 error over the unit ball of a weighted Sobolev space of dominating mixed smoothness. We are in particular interested in structured point sets from quasi-Monte Carlo methods.