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Post-doctoral Researcher

Job Description

Position Number:

126436

Title:

Post-Doctoral Associate

Functional Title:

Post-doctoral Researcher

Category Status:

15-Fac.Non-Tenured,Continuing Con

Applicant Search Category:

Faculty

University Authorized FTE:

100

Unit:

ARCH-Center for Smart Growth Research & Education

Campus/College Information:

Founded in 1856, University of Maryland, College Park is the state’s flagship institution. Our 1,250-acre College Park campus is just minutes away from Washington, D.C., and the nexus of the nation’s 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 nation’s 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.

Background Checks

Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify you from employment.

Vaccine Protocol

The University of Maryland has made the safety of our students, faculty and staff, and our surrounding communities a top priority. As part of that commitment, the University System of Maryland (USM) recently announced that students, faculty, and staff on USM campuses this fall, including UMD, are required to be vaccinated against COVID. As a prospective and/or a new employee at UMD, you will be required to comply with the University’s vaccination protocol. Proof of full vaccination will be required before the start of employment in order to work at any University of Maryland location. Prospective or new employees may seek a medical or religious exemption to the vaccination requirement at return.umd.edu and must have an approved exemption prior to the start of their employment. Failure to provide proof of vaccination or to obtain approval for a medical or religious exemption will result in the offer of employment being rescinded.

Position Summary/Purpose of Position:

 One or more fully funded 12-month post-doctoral positions are available to work with a multidisciplinary team of researchers from the University of Maryland’s National Center for Smart Growth (NCSG) and Center for Geospatial Information Science (CGIS), as well as industry and local government partners. One or more of the post-doctoral researcher(s) will have a key role in an ongoing project to model ridership across the Washington Metropolitan Area Transit Authority (WMATA) system using survey data, smart card trip data, and an array of factors related to travel, transit ridership, land use, built environment, urban form, and transport infrastructure. A key task will be to enhance a previously developed ridership model using new data sources and through the application of geospatial analytical techniques.  The post-doctoral researcher(s) could also participate in other projects currently underway at NCSG and CGIS.  

A primary task of the post-doctoral researcher(s) will be to lead model development and enhancement efforts within the UMD NCSG-CGIS team. Team members previously developed a ridership model in 2015 (https://doi.org/10.1016/j.tra.2018.06.011). However, ridership trends have since evolved and 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, roles of the postdoctoral researcher(s) on this project will include: (1) collecting and managing a database of diverse variables; (2) creating new variables based on the transportation network, points-of-interest, and neighborhood characteristics; (3) updating the currently existing model; (4) enhancing the ridership model with new variables and the incorporation of spatial dependence structures; (5) employing the ridership model to understand the factors responsible for generating ridership across different types of stations and forecasting future scenarios; and (6) drafting progress reports. The post-doctoral researcher(s) will work closely with the other project members and partnering 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. 
 
While some of the work is specific to the WMATA project, candidates will be considered for work on other projects as well.  These include projects funded by the Federal Transit Administration focused on transit-oriented development in an anticipated light rail corridor, a project funded by the Maryland Department of Transportation focused on travel behavior pre- and post-pandemic, and a project funded by the International Center for Local and Regional Development on commuting patterns in the Republic of Ireland.  Skills and experience in land market analysis, accessibility analysis, and survey methods are sought for work on these projects.
We seek highly motivated and self-driven candidates with good interpersonal skills and the ability to thrive in a diverse, multidisciplinary environment. The successful applicant will have a strong background and research experience in a transportation-related field.

The position(s) will be available immediately after becoming filled. The initial appointment is for 1 year and continuation is contingent upon funding availability. 
 

Minimum Qualifications:

?        A PhD or equivalent University level experience in geography, urban planning, transportation engineering, computer science, information sciences, economics, or related fields.
?        Strong analytical and quantitative skills supported by strong programming experience. 
?        Previous experience with generalized linear regression modeling and spatial regression modeling. 
?        Ability to understand and work with existing code and open-source software e.g., making necessary improvements and modifications as needed.
?        Proficiency in written and spoken English and strong communication skills, both personal and academic. 

Preferences:

?        research in fixed-route public transit systems.
?        data processing software and network analytics.
?        Expertise in: (1) travel demand modeling and software such as Cube Voyager (preferred), TransCAD, and VISUM; (2) advanced transportation modeling open-source software such as MATSim (preferred) and DTALite. 
?        Experience with Python, STATA, and R programming languages.
?        spatial econometrics, multilevel models, and regionalization techniques.

Additional Information:

Applicants are encouraged to apply by July 28, 2021, but review of applications will start immediately. Please submit your application material online including the following documents: (1) a current CV, (2) a letter of motivation describing your interest, relevant experience, and research plans in relation to this project, (3) names and contact information for at least 3 professional references. For questions regarding the application process, information on the project details, position details, etc. please contact Nick Finio at nfinio@umd.edu.

Posting Date:

07/15/2021

Open Until Filled

Yes

Best Consideration Date

07/28/2021

Diversity Statement:

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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