Job Description

The candidate will work developing new models to generate synthetic population and its activity patterns while accounting for social network effects. It will require fusing datasets with different spatial and temporal resolution, such as household travel survey, call detail record, and transit smart card data.

A strong background in travel behaviour models, activity-based models, discrete choice models, and computer programming would be required to accomplish this work.

The successful applicant is expected to pursue independent as well as collaborative research, must be willing to participate in writing grant proposals and in the supervision of doctoral/postgraduate research students.

Job Requirements

• Possess a PhD in Transportation or related disciplines
• Has experience of developing and training activity-based models
• Has good understanding of travel behaviour models, statistics, and optimization.
• Is proficient in programming in Python/Java/C++.
• Strong written and verbal communication
• Open to fixed term contract