Postdoctoral Research Associate Positions
We are hiring continuously.
Postdoctoral positions are available for
dynamically changing large-scale network modeling
project at the University of Wisconsin-Madison. The
candidates will work with professor Moo K. Chung
on developing new innovative computational,
statistical and machine learning methods for
large-scale brain networks obtained from fMRI and
DTI scanners that are dynamically changing over
time. 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 neuroimaging research experience is a plus but not necessary. The candidates are expected to have emerging tract records of publishing in journals and conferences, strong analytic and writing skills and capable of working within a collaborative environment. Expertise in the following areas would be useful but not critical: large-scale computation (matrices), dynamic models (time series), topological data analysis, deep learning (Boltzmann machine), computer vision (geometry & shapes).
Interested candidates should email CV (with the name of references) and representative papers to Moo K. Chung (firstname.lastname@example.org).
We are also looking for capable graduate students
Representative papers written by graduate
students and postdocs as the first authors:
Wang, Y., Ombao, H., Chung, M.K. 2018 Topological data analysis of single-trial electroencephalographic signals. Annals of Applied Statistics, 12:1506-1534 (received ENAR paper award)
Lee, M.-H., Kim, D.-Y., Chung, M.K., Alexander, A.L., Davidson, R.J. 2018 Topological properties of the brain network constructed using the epsilon-neighbor method in patients with autism, IEEE Transactions on Biomedical Engineering, 65:2323-2333 (selected for cover art)
Lee, H., Chung, M.K.,
Kang, H., Kim, B.-N., Lee, D.S. 2011. Computing
the shape of brain network using graph
filtration and Gromov-Haudorff metric. . 6892:302-309. (selected for oral, oral acceptance