University of Maryland
Job detailsJob TypeFull-timeNon-tenureRemoteLocation3100 Hornbake Library, College Park, MD 20742+ Leaflet | OpenStreetMap contributorsFull Job Description
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Founded in 1856, University of Maryland, College Park is the states flagship institution. Our 1,250-acre College Park campus is just minutes away from Washington, D.C., and the nexus of the nations legislative, executive, and judicial centers of power. This unique proximity to business and technology leaders, federal departments and agencies, and a myriad of research entities, embassies, think tanks, cultural centers, and non-profit organizations is simply unparalleled. Synergistic opportunities for our faculty and students abound and are virtually limitless in the nations capital and surrounding areas. The University is committed to attracting and retaining outstanding and diverse faculty and staff that will enhance our stature of preeminence in our three missions of teaching, scholarship, and full engagement in our community, the state of Maryland, and in the world.
Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify you from employment.
Position Summary/Purpose of Position:
A fully funded postdoctoral position is available to work with a multidisciplinary team of researchers from the University of Marylands Center for Geospatial Information Science (
CGIS), National Center for Smart Growth (
NCSG) and, as well as industry partners (
State of Place) and local government partners (
The postdoctoral researcher will have a key role in an ongoing project to model ridership across the Washington Metropolitan Area Transit Authority (WMATA) railway system (i.e., D.C., Maryland, & Virginia) using survey data, smart card trip data, and an array of factors related to travel, transit ridership, land use, the built environment, urban form, and transport infrastructure. A key task will be to develop an enhance ridership model using new data sources and through the application of geospatial analytical techniques and machine learning. A primary task of the post-doctoral researcher will be to lead model development and enhancement efforts within the UMD NCSG-CGIS team. The goal of this project is to construct models capable of capturing how ridership trends have evolved over time as the transit system has expanded. At the same time, new built environment and ridership-related variables have become available as well as new machine learning-based approaches for modeling space-time trends. As a result, the postdoctoral researcher will assist with: (1) collect and manage a database of diverse variables; (2) create new variables based on the transportation network, points-of-interest, and neighborhood characteristics; (3) construct ridership models using regression techniques, geospatial analytical techniques, and machine learning techniques; (4) employ these ridership models to understand the factors responsible for generating ridership across different types of stations and forecasting future scenarios; and (5) draft progress reports & scholarly manuscripts for publication. The postdoctoral researcher will work closely with the project team and partner organizations on model integration, scenario development and analysis, and testing. The project will provide a unique opportunity to work in a truly multi-disciplinary and multi-organizational team composed of transportation and urban planning researchers, geospatial information scientists, local planners, and technologists.
The initial duration of this appointment is for a one year term with the potential for extension based on performance and availability of funding. This position is eligible to work remotely.
This role would be a member of the
Center for Geospatial Information Science (CGIS) and the National Center for Smart Growth (
NCSG), in addition to the
Department of Geographical Sciences. As such, the postdoctoral researcher would be encouraged to interact with these broader communities and potential collaborate on additional projects.
Education and Experience
A PhD in geography, urban planning, transportation engineering, computer science, information sciences, economics, or related fields;
Previous experience modeling using generalized linear regression, multilevel regression, or spatial regression;
Knowledge, Skills, and Abilities
Demonstration of strong analytical and quantitative skills;
Basic knowledge of machine learning techniques;
Proficient with Python and/or R programming languages
Excellent English oral, written, and interpersonal communication skills.
Preferred Knowledge, Skills, and Abilities
Prior research in fixed-route public transit systems;
Active interest in issues at the nexus of transportation, land-use, and urban analytics;
Previous experience leading data processing and analysis efforts;
Demonstrated ability to contribute to existing code and open-source software;
Expertise in travel demand modeling software (e.g., Cube Voyager or TransCAD) or open-source transportation modeling software (i.e., MATSim and DTALite);
Advanced knowledge of spatial analysis, econometrics, and/or machine learning.
should include a brief (1 page) cover letter, a resume or CV, and the names and contact details (including phone and e-mail) of 3 references.
For best consideration, applications should be submitted by November 30th, but the search will continue until the position is filled.
For questions about the position, please contact Dr. Taylor Oshan at firstname.lastname@example.org. Further information on this position and on academic and research programs of the Department can be found at
http://www.geog.umd.edu. Applications from women and minorities are particularly sought.
Open Until Filled
Best Consideration Date
The University of Maryland, College Park, an equal opportunity/affirmative action employer, complies with all applicable federal and state laws and regulations regarding nondiscrimination and affirmative action; all qualified applicants will receive consideration for employment. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, religion, sex, national origin, physical or mental disability, protected veteran status, age, gender identity or expression, sexual orientation, creed, marital status, political affiliation, personal appearance, or on the basis of rights secured by the First Amendment, in all aspects of employment, educational programs and activities, and admissions.
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