Opportunities for Graduate & Professional Students
Institute of the Study of Societal Issues: Graduate Fellows Program
The Institute for the Study of Societal Issues serves as the research and teaching base for the Graduate Fellows Program. Fellows attend a weekly two-hour seminar in which they receive practical training in theory, methods, and policy work. The core emphasis is on field research and the production of scholarly work geared towards a broad understanding of the patterns of social change in the structures, social practices, and culture of U.S. cities. The training draws on insights from a wide array of fields, including sociology, urban anthropology, political science, urban and regional planning, geography, education, history, public policy, law, social welfare, and public health. Some Fellows also participate in ongoing research conducted by the Institute and its affiliated faculty.
Closing Date: April 1st
USGS/NASA: “Hyperspectral Data Products for Ecosystem Science & Natural Resource Management.” Postdoc Research Position
Moffett Field, Mountain View, CA.
USGS and NASA have launched an interagency postdoctoral fellowship. This jointly-funded pilot program will bring researchers to Silicon Valley to explore the application of cutting edge science and technology to some of our Nation’s biggest challenges. Fellows will have the opportunity to work with NASA, NGA and industry leaders in the Silicon Valley
We are partnering because we believe that progress on the daunting challenges facing our nation requires an orbit-to-core approach. This effort is being led by the USGS National Innovation Center (NIC) and NASA’s Ames Research Center.
USGS Land Imaging, Energy and Minerals Programs and the Water Mission Area are matching support provided by NASA and NGA Silicon Valley for the first cohort of fellows. For more information, or to support additional opportunities, please contact the USGS NIC Director Jonathan Stock (email@example.com) or NASA Ames Research Center’s liaison, Ian Brosnan (firstname.lastname@example.org).
Closing Date: April 29th
University of Michigan: Postdoctoral Research Fellowship Statistics and Infectious Disease Ecology
Applications are invited for a postdoctoral research position working on a new NSF/NIH-funded project at the interface of statistics, modeling, and epidemiology aiming to develop powerful new methods for spatiotemporal dynamics and to combat dengue fever. Specifically, the postdoc will be part of an interdisciplinary team working to develop statistical methodology for partially observed spatiotemporal dynamic systems and to use this methodology to infer dynamic models that explain and predict patterns of dengue incidence in Rio de Janeiro.
The postdoctoral fellow will be jointly supervised by Profs. Edward Ionides (Statistics) and Aaron King (Ecology & Evolutionary Biology, Complex Systems, Mathematics) at the University of Michigan. The project team also includes Prof. Mercedes Pascual of the University of Chicago. These researchers have long experience in methods development, implementation, and application in infectious disease epidemiology and ecology. The University of Michigan consistently ranks among the leading universities worldwide and has top-tier graduate programs in statistics, ecology & evolutionary biology, and epidemiology. Ann Arbor is also routinely rated one of the best places to live in the U.S. due to its affordability, lively culture, and natural beauty.
Applicants should have a doctoral degree in Statistics, Epidemiology, Ecology, Applied Mathematics, Computer Science, or a related field, a good record of scholarly publication, and excellent written and oral communication skills.
Compensation and start date. The salary is $53k per year and comes with the standard University of Michigan benefits package. Two years of funding are available, contingent on adequate progress during the first year. The start date is negotiable, with a target of July 2019.
Detailed project summary. Statistical analysis of partially-observed, nonlinear, stochastic spatiotemporal systems is a methodological challenge. Existing inference algorithms suffer from a “curse of dimensionality” that prohibits their applicability to models describing interacting dynamic processes occurring within and between many spatial locations. In this project, new inference algorithms will be developed, and shown in theory and in practice to advance capabilities for spatiotemporal data analysis. Methodological research will be carried out in the context of addressing transmission of dengue virus within a tropical megacity. Global incidence of dengue has risen 30-fold over the past fifty years, with notable geographical expansion in South and Central America. The municipality of Rio de Janeiro is a focal point for dengue transmission in this region. Spatiotemporal data on dengue cases in Rio de Janeiro will be analyzed, together with data on human movement, temperature, and rainfall. Policy decisions for the detection, control, and potential eradication of infectious diseases are best informed by model-based understanding of disease transmission. Improved understanding of the spatiotemporal dynamics of disease transmission will have implications for improvements in disease control. Mathematical models will be developed to describe spatiotemporal dynamics of dengue transmission, and the novel statistical methodology will be used to link these models to the data from Rio de Janeiro.
The University of Michigan is a Non-Discriminatory/ Affirmative Action Employer. Individuals from underrepresented groups are especially encouraged to apply.
Application Details: please submit a single PDF document containing (1) cover letter including the names and contact information of three references, (2) curriculum vitae, (3) two representative papers, by email to