We expect to have two postdoctoral positions available for January 2014 (or later). These positions are in David Blei's research group in the Computer Science Department at Princeton University. They are one-year positions with likely renewal to two years. They are for doing basic research in probabilistic modeling.
We will have two main research thrusts:
(a) Developing new scalable methods of approximate posterior inference. We are interested in developing generic variational methods for massive data sets and streaming data sets. For example, see our recent work on stochastic variational inference (Hoffman et al., 2013) and nonconjugate variational inference (Wang and Blei, 2013).
(b) Developing new methods for calculating model fitness and new ways of diagnosing model misfit. We are interested in developing modern methods related to predictive sample re-use (Geisser, 1975) and posterior predictive checks (Rubin, 1984; Meng, 1994; Gelman et al. 1996).
We will implement these ideas in modern probabilistic programming systems and exercise them in several problem domains. (Though the work is for general methodological research, we encourage applicants who are already interested in specific applied problems.) More broadly, our goal is to tighten the probabilistic modeling pipeline---posit a model, estimate a posterior, check the model, revise the model---in the service of scientific and technological applications.
QUALIFICATIONS
Applicants should have a PhD and experience with applied probabilistic modeling. Our research will be in statistical machine learning, but we happily will consider applicants fields outside of Computer Science and Statistics (e.g., Physics, Biology, Social Sciences, Astronomy, etc.)
TO APPLY
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