University of WisconsinMadison
Location
Madison, WI | United States
Job description
Postdoctoral positions are available in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison () . The candidates will work with professor Moo K. Chung ( on developing new innovative mathematical, statistical or computational methods in modeling large-scale brain images and networks obtained from structural and functional MRI.
Candidates should be familiar with one of following ares: network science, functional data analysis, spatial statistics, topological data analysis, dynamical systems, time series analysis, graph theory, diffusion learning, partial differential equations (computational). The following research topic is current research focus and will be given the highest priority: 1) Wasserstein distance, Maximum mean discrepancy (requires theoretical skill - development of mathematical theory and theorem proof), 2) Reaction-diffusion equation on manifolds (requires computational skill - solving reaction diffusion equation using on triangulated meshes and irregular domains), 3) Topological data analysis (Euler characteristics, Betti numbers) on Ising models and dynamical systems.
Candidates should have received or expected to receive PhD degree or equivalent in mathematics, physics, CS, EE, statistics, biomedical engineering, psychology, neuroscience or related areas. Previous imaging research experience is a plus but not necessary but programming skill is a must. Interested candidates should email CV (with the name of references) and representative papers to [email protected]. No consideration will be given to candidates without publications.
There is no deadline for the positions and we are hiring year round. However you should apply for the position immediately for chance. The following are two recent publications in leading journals from our group: Topological learning on networks (Annals of Applied Statistics: arXiv: 2012.00675), Hodge Laplacian for higher order network connectivity (IEEE Transactions on Medical Imaging: arXiv:2110.14599).
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