POSTDOCTORAL FELLOW, COMPLEXITY SCIENCE, Urban Studies and Planning-Senseable City Lab (SCL) (multiple positions), to work on the modeling and analysis of complex urban systems. Will perform basic and applied research, using and extending existing methods and tools; actively contribute to the design and initiation of new research projects in the field of complex urban systems; integrate specific research findings with the work of all project partners, e.g., Santa Fe Institute, Cornell University, and MIT CSAIL; present research results at international workshops and conferences; and coauthor articles for publication in leading peer-reviewed journals.
Since its inception, SCL has acquired massive and unique data sets about human behavior in cities all over the world. The lab has now launched a major interdisciplinary initiative to harness these unprecedented data sets in order to better understand cities as complex systems. The available data is very broad and includes individualized telecommunication records, GPS traces from different modes of transportation, electronic medical records, and detailed credit card transactions among others.
REQUIRED: Ph.D. in physics, mathematics, computer science, engineering, computational sociology (social network analysis), or related field. Those with an interdisciplinary mathematical modeling background will be given particular attention. Experience handling large-scale data sets and complex systems modeling and analysis a plus. Must present a strong publication record. Job #10094
For further information or an informal discussion about the post, please contact Professor Carlo Ratti at 617-324-4474 or email@example.com.
In addition to applying online with a CV and cover letter, please submit a CV, contact information for three referees, a one-page statement indicating interest in working with SCL, and one or two representative publications describing your research to firstname.lastname@example.org.
MIT is an equal employment opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, sex, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, ancestry, or national or ethnic origin.