Postdoc position in machine learning at UW-Madison

[Moo K. Chung]

Multiple NIH/NSF funded postdoctoral positions are available at the University of Wisconsin-Madison. The candidates will work with professor Moo K. Chung (www.stat.wisc.edu/~mchung) on developing innovative machine learning methods for brain image data obtained from magnetic resonance imaging. 

Candidates should have received or expected to receive PhD degree or equivalent in Computer Science, mathematics, physics, Electrical Engineering, statistics or related areas. Previous imaging research experience is a plus but not necessary. Interested candidates should email CV (with the name of references) and representative papers to  mkc...@wisc.edu. Candidates without publications in leading journals or conferences will not be considered.

We are currently looking for candidates familiar with diffusion learning, reaction-diffusion equations, topological data analysis, dynamical systems or Ising models. The development of scalable software solutions for end users is essential. Thus, a deep understanding of the underlying mathematics and the ability to code learned mathematics in a principled fashion quickly is crucial. We do not use existing machine learning tools such as TensorFlow; instead, we code all necessary routines from scratch.

There is no deadline for these positions and we are hiring year-round. However you should apply immediately for for a chance to be hired this year. Below are two recent publications in leading journals: Topological learning on network data (Annals of Applied Statistics: arXiv: 2012.00675), Hodge Laplacian for network data (IEEE Transactions on Medical Imaging: arXiv:2110.14599).