This position is focused on development and application of methods for the analysis of high-dimensional data, with an emphasis on scalable Bayesian methods. The fellow will work under the direction of Professor David Dunson in the Department of Statistical Science at Duke University. Methods will be motivated by a variety of applications with a particular emphasis on neurosciences, including relating brain networks to behavior, abilities and neuropsychiatric diseases. The overarching emphasis is on developing transformative probabilistic models and computational algorithms, which change routine practice in analyzing data sets in the motivating applications, lead to new biological insights, have theoretical guarantees, and can be applied in broad high dimensional low sample size applications.
The fellow will work closely with neuroscientists and other scientific collaborators.
This position is for 1-2 years with the possibility of extension, and there will be the opportunity to gain teaching experience in Statistical Science including at the undergraduate level. The ideal candidate will be finishing a PhD in Statistics, Computer Science, Mathematics or a related field and will have a combination of an excellent background in theory and computation, ideally complemented with some knowledge of Bayesian statistics. Please contact David Dunson via email at This email address is being protected from spambots. You need JavaScript enabled to view it. to submit an application and make inquiries. Applications will continue to be collected until positions are filled.
