Applications received after this date will be reviewed by the search committee if the position has not yet been filled.
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
Transportation Technology and Policy (TTP) is an interdisciplinary Graduate Group administered through the Institute of Transportation Studies (ITS). It offers three degrees: MS Plan I, MS Plan II, and Ph.D. The TTP program provides an opportunity to do interdisciplinary research to address pressing transportation, environmental, economic, policy and social challenges facing California, the United States, and the world with students coming from a variety of disciplines to pursue either a technology or policy track.
The TTP graduate curriculum draws on a multitude of academic disciplines and the group utilizes participating faculty or temporary faculty to staff courses to maintain a top-quality academic program. The following describes our search procedures and selection process for re-appointment or for hiring new temporary faculty.
Courses Open for Recruitment, Academic Year 2017-18
TTP 289A, Applied Data Analysis
The primary purpose of this course is to provide students with the tools needed to conduct graduate level analysis of data. Our course will cover two aims:
- Statistical toolset to analyze data
- Immersion in data analysis on real datasets Some aptitude in programming (preferably R, Python, or MATLAB) is preferred.
The first two weeks of the course will briefly review programming across platforms in R and Python. The course will quickly cover basic functions, custom functions, operational scaling, and data handling.
The course then delves into basic data analysis operations, including basics of examining and inspecting data (identifying data types, dealing with missing data and outliers, maintaining data integrity). The course will cover a range of regression analysis including parametric (OLS), semi-parametric (logistic), and non-parametric (GLM, kernel regressions) regressions. The course will introduce simple concepts such as bootstrapping and Monte Carlo as well as more advanced techniques including an introduction to machine learning.
Lastly, the course will apply the learned techniques to real data. The course will cover a variety of datasets as examples (see potential datasets below) to demonstrate how to use the software tools in reality. The datasets will include both transportation and energy based data. The course will examine any dataset based on students’ interests (including data selected by the students).
TTP 289A, Discrete Choice Modeling
The objectives of this course are to understand the behavioral, statistical, and econometric foundations for the formulation and estimation of discrete choice models, explore a variety of discrete choice models and their application to travel demand forecasting and related subjects, and gain experience in the formulation, interpretation, and evaluation of discrete choice models using empirical data and a variety of statistical packages including both commercial software and open-source packages.
Through this course, the students will gain knowledge of the basic theory of discrete choice models and will develop skills to specify, estimate, and interpret basic discrete choice models. Depending on the previous experience and background of the students, and the other courses offered at UC Davis, more advanced applications of discrete choice modeling could be considered for inclusion in this course.
Curriculum Vitae - Your most recently updated C.V.
Statement of Research (Optional)
Statement of Teaching
Statement of Contributions to Diversity - Diversity contributions documented in the application file will be used to evaluate applicants. Visit http://academicaffairs.ucdavis.edu/diversity/equity_inclusion/index.html for guidelines about writing a diversity statement and why one is requested.
How to apply
- Create an ApplicantID
- Provide required information and documents
- If any, provide required reference information