For full consideration, please provide the following in your application document: (a) cover letter, (b) CV, (c) writing sample (e.g., working paper, publication, dissertation chapter) approximately the length of a publishable research paper (max 10,000 words), and (d) two letters of reference. Ideally, the writing sample will be on a topic related to the substantive focus of the position (violence, elections, contentious politics), or includes some of the methodological approaches mentioned below. Please note that the UM Career Portal will only allow you to attach one document, so all your application materials--with the exception of confidential letters of reference--must be combined into one PDF file for submission. Confidential letters of reference can be sent directly to [email protected], with 'SUNGEO Postdoc: Recommendation for [NAME]' in the subject line."
For questions about this position, you may contact the SUNGEO research project manager, Julia Lippman, by email to [email protected]
The Center for Political Studies (CPS; http://www.isr.umich.edu/cps), part of the Institute for Social Research (ISR) at the University of Michigan, is seeking to hire a full time post-doctoral research fellow to assist in developing data infrastructure for social scientists. The person will work on methodological topics including but not limited to spatial econometrics, geostatistics, machine learning, image processing, and substantive topics including elections and political violence. Beyond these core duties, the selected candidate will receive professional mentorship from CPS faculty, opportunities to present research in progress at workshops and events hosted at the center, along with opportunities for collaborative research with University of Michigan faculty and graduate students. CPS will provide ample opportunities for the fellow to learn new skills and develop greater proficiency in the technical aspects of the position.
This is a 100% (full-time) position where the selected person will work 50% on an NSF-funded project to develop the Sub-National Geospatial Archive System for Social and Behavioral Data (SUNGEO) and 50% on research activities of their own choosing. The SUNGEO project involves building a suite of software tools to integrate spatially-misaligned data, to account for differences in measurement across primary sources, and to facilitate the assessment of the generalizability and robustness of statistical results across data sources, scales, integration methods, and geographic/historical contexts. The person will assist the leadership team of SUNGEO in design and implementation of new tools for dataset development, creation, and archiving.
Ph.D. in a relevant discipline such as social science, statistics, computer science, information systems, geography, or similar
Training in statistics, computer science, and/or quantitative methods
Experience in the use of geospatial tools
Demonstrated ability and willingness to learn new technical research skills and methods
Interest in interdisciplinary research
Proficiency in Python and/or R
Experience with geospatial data analysis
Experience with Bayesian inference and/or machine learning
The appointment will be for twelve months, with flexible start and end dates starting no later than September 1, 2020 and ending around August 31, 2021.
The salary range for the position is $65,000 - $70,000, depending upon skillset and experience, and includes the University of Michigan post-doctoral research fellows benefits package outlined here:
This posting will be posted for a minimum of fourteen (14) calendar days. This opening may be removed from posting boards and filled any time after the minimum posting period has ended.
The Institute for Social Research (ISR) at the University of Michigan seeks to recruit and retain a diverse workforce as a reflection of our commitment to serve the diverse people of Michigan, to maintain the excellence of the university, and to ground our research in varied disciplines, perspectives, and ways of knowing and learning.
The University of Michigan is an equal opportunity/affirmative action employer.
Internal Number: 183112
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