Job Summary
Applications are sought for a 2 year postdoctoral position at the Department of Statistics, Purdue University, under the supervision of Dr. Anindya Bhadra.
Company Information
The Department of Statistics at Purdue University is a leading center of statistics research (most recently ranked #22 on the US News and World Report ranking of Statistics graduate programs), and Purdue University, an AAU Member, boasts of exceptionally strong science, engineering and agriculture programs. The university is located in West Lafayette, in a safe small town setting with reasonable cost of living, approximately 60 miles northwest of Indianapolis and 110 miles southeast of Chicago, both easily reachable by car or public transport. Purdue University is an Equal Opportunity/Affirmative Action Employer.
Responsibilities
The postdoctoral fellow will be mentored by Dr. Anindya Bhadra (https://www.stat.purdue.edu/~bhadra/ ) and will have an opportunity to choose from exciting projects ranging from robust Bayesian analysis of graph and network structured data, analysis of limiting behavior of deep neural networks and their connections with Gaussian and non-Gaussian stochastic processes, and highly multivariate stochastic processes; with applications ranging from spatial transcriptomics, precision medicine and the analysis of spatial and spatiotemporal data. The initial appointment is for one year, renewable for one additional year subject to satisfactory performance. The postdoctoral fellow will be mentored in all aspects of manuscript preparation and grant proposal writing. Substantial opportunities for collaboration exist at Purdue and beyond. Support is available for dissemination of research in national and international conferences. Teaching is not required, but can be arranged if desired. The start date is negotiable, but could be as early as Summer 2024, and search will continue until the position is filled.
Position Qualifications
We seek exceptionally strong candidates with a PhD in statistics, biostatistics, computer science or a related field. Candidates with demonstrated accomplishment in academic research, as can be judged from published papers or publicly available (e.g., arXived) preprints, will be given priority. Strong programming skills in R/Python/MTLAB with some knowledge of C/C++ are preferred. Prior experience and demonstrated interest in Bayesian methodology is a plus.