The Urban Information Lab (UIL) at the University of Texas at Austin is recruiting a postdoctoral fellow to lead the development, implementation, and deployment of an Urban Digital Twin project. This project supports UIL’s urban major investment in assessing urban planning and policy-making. A successful candidate is responsible for developing a multi-facet AI-enhanced digital twin environment that enables urban data integration, forecasting, and policy evaluation, specifically for the city of Austin. Specifically, in part one of this project, the successful candidate helps in developing a live fire incident map and air pollution and heat dissipation forecast model. The successful candidate has the opportunity to work across disciplines, mentor graduate and undergraduate students, join the Good Systems community, and get access to computing nodes at the Texas Advanced Computing Center (TACC).
UIL is a cross-disciplinary lab and a national leader in using emerging information technologies to better understand, measure, plan, and develop urban environments and assess urban policies. Since the lab’s inception in 2013, lab researchers have led major projects including investigating Uber’s price surge in Austin, quantifying the spatial distributions of Airbnb rentals in 50 major US cities, exploring bike sharing in NYC, Chicago, DC, Boston, Dallas, San Antonio, and Austin, identifying transit deserts in 52 US cities, and evaluating people’s access to food sources in major Texas cities, among other projects. UIL stakeholders include municipalities, non-profits, and the private sector.
Eligibility: Applicants must be outstanding, intellectually curious researchers who will be ready to pursue independent research. They should have a doctorate in a related area before the expected start date of their postdoctoral fellowship. Collaboration experience and strong communication skills will be viewed favorably. We recognize that strength comes through diversity and actively seek and welcome people with diverse backgrounds, experiences, and identities. Individuals already at the University of Texas at Austin are eligible to apply.
Faculty Mentors: The fellow will be supervised by three faculty mentors. Dr. Junfeng Jiao will be the major supervisor, Dr. Farahi and Dr. Navratil will be the co-supervisors. The fellow will closely collaborate with Dr. Junfeng Jiao, Dr. Arya Farahi, and Dr. Paul Navratil. The fellow has the option to sit in the School of Architecture, the Department of Data Science and Statistics (SDS), or the Texas Advanced Computing Center (TACC). The mentors will provide career guidance, meet with the fellow regularly to assess research progress, help the fellow seek opportunities for career advancement, and participate in activities that strengthen the fellow’s community and the University of Texas at Austin community, including Good Systems and Machine Learning Laboratory.
Principal responsibilities include:
- Leading the implementation of the digital twin project.
- Write scholarly work.
- Present the results at conferences and seminars.
- Lead and develop funding proposals.
- Mentor graduate and undergraduate students.
- A Ph.D. degree in Transportation Engineering or other Engineering fields, Computer Science, Data Science, Urban Planning, or a related discipline (or equivalent combination of qualifications and experience)
- Strong publication records demonstrated by peer-reviewed journal papers or peer-reviewed conference papers.
- Experience with machine learning tools and programming languages, including Python, R, C#, and/or C++
- Demonstrable writing abilities through publications and good communication skills.
- Experience with client-server web programming and design.
- [optional] Experience working with interdisciplinary, collaborative teams, and managing research assistants is advantageous.
Stipend: $65,000-$72,000 is available in salary support per year for an initial two-year appointment. Appointments may be extended for a third and fourth year, subject to budget availability and performance.
Travel: An additional $3,000 will be allocated for research and travel expenses each year.
Relocation expenses: Up to $1,500 Funding is available to support relocation expenses for fellows moving to the Austin, TX area.
Benefits: UT Austin has an excellent benefits package as well as a number of policies and programs to support employees as they balance work and family, if applicable.
The Application Process.
We will begin review of applications starting June 21, 2023 and will continue until the position is filled.
The application package should include a cover letter, a CV, a statement of research interests, and the names and contact information of three individuals who are willing to supply letters of support.
- 1. The candidate’s C.V.
- 2. In the cover letter, the applicant should identify the host faculty mentor.
- 3. Research Statement must be up to 2 pages, that includes: A. Qualifications that make the applicant particularly suitable for this program; B. Prior relevant research; And C. the applicant’s career plan.
- 4. Names and contact information of three references who are willing to supply letters of support and their relation to the applicant.
- 5. [optional] A link to a professional website where published papers, preprints, and software packages are available may be provided on the candidate’s CV.
- Priority deadline: 11:59 pm, June 20, 2023. Applications received after this date will be considered only if the position is still available.
- Notifications for interviews will start on July 1, 2023.
- The expected start date is August 1st, 2023, however, this is negotiable. If an earlier or later start date is of interest, please let us know at any point during the application process.
For questions about this application, please contact Saleh Afroogh at email@example.com
Equal Employment Opportunity Statement
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.