Columbia University is looking for a postdoctoral researcher for a project aimed at the modeling and analysis of urban systems using big data created by human activity in the context of disaster risk management. This research fellowship is guaranteed for 1 year and renewable for a second year. At Columbia, we have a rich community of researchers investigating human-natural systems, and we believe the post-doc will find the community of scholars an inspiring one. The central research question is how to obtain and utilize big data on human mobility from smartphones, social media, and public transportation records as well as to associate human mobility with natural hazard data to make disaster planning data-driven. Other cities in the project are Tokyo and Taipei with teams from Japan and Taiwan as project partners.
This project aims to design a dynamic disaster response system accounting for complex disaster risks and multiple scenario models by harnessing large-scale data and conducting field surveys in and around major stations in metropolitan areas in Tokyo, New York, and Taipei. Traditionally, disaster planning has relied on limited scenarios regarding possible disaster scenarios. Notably, past planning efforts often do not distinguish between event time of day, workdays vs. weekends, seasons, or urban locations (e.g., indoor, outdoor, underground). Furthermore, traditional approaches have failed to capture the diverse needs of the affected social groups. The project will address dynamic disaster scenarios and the needs of vulnerable socioeconomic groups in urban areas. To do so, the project will integrate both scenario-based and data-driven planning while incorporating sensor data and stakeholder engagement. The project will take a mixed-methods approach while synthesizing data from various sources, such as from smartphones and in-depth interviews.
- Perform applied research on quantifying, modeling, and predicting human behavior within the urban environment, including mobility, social interactions, environmental hazards, etc. in collaboration with the multidisciplinary team, external research, and industrial partners.
- Analyze big datasets created by human mobility and activity.
- Participate in applied projects with stakeholders.
- Actively contribute to the design and initiation of new research projects and ideas in the field of urban systems and disaster risk management.
- Present research results at top conferences.
- Co-author articles for publication in leading peer-reviewed journals and top conferences.
The successful candidates must hold a Ph.D. (or nearing completion) in computer science, engineering, computational social science, or a related field. Candidates would have technical knowledge of human mobility as well as modeling and data analytic capabilities to analyze big data on human mobility. Candidates with modeling and data analytic backgrounds are also given particular attention. The ability to work with multidisciplinary and multicultural teams (urban planners, computer scientists, social scientists, and environmental engineers from Asian countries) and familiarity with existing data and urban infrastructure in the US settings is a plus.
- Experience in handling large datasets with strong statistical analysis and modeling backgrounds is required
- Candidates must present a strong publication record.
- Practical skills in R, Python, or Matlab are expected. Other relevant technologies are a plus.
- Experience in human mobility is a definite plus.
- Experience in Machine learning or statistics is a plus.
- Experience in working with stakeholders (e.g., planners, city officials, infrastructure officials) in the US context is a plus
- Advanced verbal and written English skills are required.
To apply, please submit the following materials:
- Resume or CV
- Cover letter addressed to the Search Committee briefly describing your qualifications, professional goals, and specific interest in this position.
- Contact information for 3 references.
Please submit your application to Dr. Masahiko Haraguchi ([email protected]). Position will start in October 2020 pending release of funds from the National Science Foundation.