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Postdoctoral position in statistical modeling of social networks

A full-time postdoctoral position is available beginning Fall 2014 in the research group of Tian Zheng and Andrew Gelman working on statistical analysis and modeling of social network data, in close cooperation with our experimental collaborators. Four key papers of this project so far are:

http://www.stat.columbia.edu/~gelman/research/published/overdisp_final.pdf

http://nersp.osg.ufl.edu/~ufruss/documents/mccormick_salganik_zheng10.pdf

http://www.stat.columbia.edu/~gelman/research/published/DiPreteetal.pdf

http://arxiv.org/pdf/1301.2473.pdf

Requirements: The work is highly interdisciplinary, and applicants must have strong statistical and computational skills. Social science research skills are preferred but not necessary. Preferred educational background is a PhD in statistics, computer science, political science, sociology, or a related field. Expertise in Bayesian modeling and computing is required. Previous experience with network data is preferred but not required.

Environment: The research group is at Columbia University, based in the Statistics department and closely integrated with the Applied Statistics Center at Columbia, in the great city of New York. There will also be the opportunity to work with Zheng and Gelman and their collaborators on other research projects involving statistical computing, genetics, and computational social science.

Appointment: The initial appointment will be for one year, and is renewable. Salaries will be set based on experience and skills.

Applicants should send email to tzheng@stat.columbia.edu providing:

• a one-page description of past research experience
• a one-page description of future research interests and goals
• a resume of educational and research experience, including publications
• three letters of reference

5 Comments

  1. nlkm says:

    is a PhD a requirement? If so, is there any RA position open before 2014? Thanks.

  2. JSB says:

    How will your team be addressing the causal aspects of network analysis? Network analysis of social connections seems to be widely adopted with little critical thought about its limitations. On the contrary, I see examples everywhere of strong (but in fact facile) causal inferences being made based on little more than summing up nodal connections. Is there a Bayesian approach to help improve the causal analysis of social networking typologies (akin to something more robust, such as SEM)?

  3. Jro says:

    “Salaries will be set based on experience and skills.” can we still have a rough idea, like salary range for a good fit right after PhD, and salary range for a good fit with 5 years of experience? Thanks!