Andrew Gelman (Columbia University) and Jennifer Hill (New York University) seek to hire a post-doctoral fellow to work on development of iterative imputation algorithms and diagnostics for missing-data imputation. Activities would include model-development, programming, and data analysis. This project is funded by a grant from the Institute of Education Sciences. Other collaborators on the project include Jingchen Liu and Ben Goodrich. This is a two-year position that would start this summer (2011) or earlier if possible.

The ideal candidate will have a statistics or computer science background, will be interested in statistical modeling, serious programming, and applications. He or she should be able to work fluently in R and should already know about hierarchical models and Bayesian inference and computation. Experience or interest in designing GUIs would be a bonus, since we are attempting to improve our GUI for missing data imputation.

The successful candidate will become part of the lively Applied Statistics Center community, which includes several postdocs (with varied backgrounds in statistics, computer science, and social science), Ph.D., M.A., and undergraduate students, and faculty at Columbia and elsewhere. We want people who love collaboration and have the imagination, drive, and technical skills to make a difference in our projects.

If you are interested in this position, please send a letter of application, a CV, some of your articles, and three letters of recommendation to the Applied Statistics Center coordinator, Caroline Peters, [email protected]. Review of applications will begin immediately.