Postdoctoral Fellow in Big Data and Community Health The University of Michigan announces a one-year postdoctoral fellowship position. The position will start May 1, 2015. Salary: $50,000 per year, plus a competitive benefits package and the opportunity to appoint and supervise one or more paid undergraduate research assistants to work on projects of your choice. To apply Candidates should submit the following materials electronically to Prof. Tiffany Veinot at <[log in to unmask]<mailto:[log in to unmask]>> Email <[log in to unmask]<mailto:[log in to unmask]>> one PDF file which includes 1. A statement of interest describing your relevant background and skill 2. A current curriculum vitae 3. The name and contact information for three references. (One reference should be your doctoral advisor.) 4. Letters of recommendation will only be solicited from finalists 5. Two publications or other writing samples Review of applications will begin immediately and continue until the position is filled. Position Description The School of Information, School of Public Health and Urban and Regional Planning Program (at the Taubman College of Architecture and Urban Planning) are jointly offering a postdoctoral fellow position in the multidisciplinary area of “big data and community health.” The burden of negative health outcomes is, unfortunately, differential in the United States (US). Living in an area in which a large proportion of residents are socio-economically disadvantaged exerts an independent, negative effect on individual health status. Initiatives to reduce such neighborhood-based health disparities, or “neighborhood effects”, require access to meaningful, timely, and actionable information regarding the health of different groups, and factors that influence their health. Yet, there are key gaps in the country’s population health information infrastructure, including a lack of accepted measures of community health and the fact that many existing data are not fully exploited nor effectively linked. In part, these gaps are due to the challenges of gathering and analyzing large, diverse, dynamic, and relevant data sets. The project will address these challenges by leveraging emerging “big data” sources such as social media sites and citizen-created maps, while linking new sources with existing health data sets. The postdoctoral fellow will help lead the efforts to: 1) Collect, process, and analyze geo-tagged social media data to measure neighborhood characteristics that are related to health disparities; 2) Compare social media measures with other existing data sets; and 3) Combine machine learning and spatial statistical techniques to explore and model the relationship between neighborhood characteristics and health behaviors. This position will fund a researcher who will have the opportunity to work alongside an interdisciplinary team of collaborators to develop “big data and community health” as an area of research. The team of investigators includes: Dr. Tiffany Veinot (School of Information and Department of Health Behavior and Health Education, School of Public Health), Dr. Robert Goodspeed (Urban and Regional Planning Program, Taubman College of Architecture and Urban Planning), Dr. Veronica Berrocal (Department of Biostatistics, Dr. Daniel Romero (School of Information and Department of Computer Science, College of Engineering) and Dr. Phillipa Clarke (Institute for Social Research). The postdoctoral fellow will be an equal member of the interdisciplinary research group. The postdoctoral fellow will be expected not only to conduct independent research, but also to collaborate actively in the aforementioned research project with faculty, graduate students, and undergraduate research assistants. This responsibility includes regular communication and coordination with the project team. The postdoc will also be expected to contribute substantially to publications related to “big data and community health,” acting as first author on some and as a secondary author on others. The postdoctoral fellow will have office space at the University of Michigan and may have the opportunity to teach one course in the School (to be negotiated). Qualifications: · A Ph.D. in a related area completed by the position start date. The ideal candidate will have a PhD in Computer Science, Statistics, Mathematics, Information, Public Health, Geography or a related field. · A strong background and experience with machine learning, data mining, and/or spatial statistic methods. · Programming experience and comfort with handling and analyzing big data sets. · Motivation and initiative, excellent communication skills, and the ability to work independently as well as in a team. · A desire to learn and contribute to the field of Public Health is preferred. Non-Discrimination Policy Notice The University of Michigan, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University of Michigan is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, height, weight, or veteran status in employment, educational programs and activities, and admissions. Inquiries or complaints may be addressed to the Senior Director for Institutional Equity, and Title IX/Section 504/ADA Coordinator, Office of Institutional Equity, 2072 Administrative Services Building, Ann Arbor, Michigan 48109-1432, 734-763-0235<tel:734-763-0235>, TTY 734-647-1388<tel:734-647-1388>. For other University of Michigan information call 734-764-1817<tel:734-764-1817>. Apply here: http://umjobs.org/job_detail/106604/research_fellow Application deadline: February 28, 2015 For more information, contact: Tiffany Veinot, [log in to unmask]<mailto:[log in to unmask]> _______________________________________________ CITASA mailing list [log in to unmask]<mailto:[log in to unmask]> http://list.citasa.org/mailman/listinfo/citasa_list.citasa.org