Mentored by Sayan Mukerhjee & Ivo Sbalzarini
at TU Dresden or Leipzig University (please indicate preference)
We will consider the integration of classical stochastic modeling (including numerical methods, Bayesian statistics, and inverse problems approaches) with modern AI methodology. The idea is to build more efficient, reliable, and interpretable algorithms that also quantify uncertainty. We will consider variational formulations of various learning problems as a starting point to solve challenging inverse problems in complex settings as well as solving challenging sampling problems central to many inference and learning applications. We will also use ideas from statistical physics and dynamical systems to develop algorithms.
You will have access to the machine-learning HPC resources of the ScaDS.AI Center for Scalable Data Analytics and AI Dresden/Leipzig, offering state-of-the-art CPU (Intel and IBM POWER 9) and GPU (Nvidia A100 and V100) resources, as well as high-performance computing and storage systems for computational experiments. You will also be working with the machine learning and AI people at the Center for Systems Biology Dresden (CSBD), a joint center between the Max Planck Society and TU Dresden, and the Interdisciplinary Center for Bioinformatics, a similar joint center between the Max Planck Society and Leipzig University.
The candidate will need a solid background in mathematics (an emphasis on high-dimensional statistics, functional analysis or topology will be an advantage). Since the work will require extensive computational experimentation, proficiency in scientific programming and interest in data analysis are necessary.