Transport Planning Research Fellow - Data-Driven Models

National University of Singapore; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

The National University of Singapore invites applications for a Research Fellow (Transport Planning) in the Civil and Environmental Engineering department at Kent Ridge Campus. The role focuses on developing destination choice models for future land use and transport systems using data-driven approaches.

The ideal candidate will lead funded projects, supervise research students, and contribute to proposals and reports, employing Python/Java/C++ and software like EMME or PTV Visum.

Qualifications

• PhD in a related discipline by December 2024.
• Strong knowledge in discrete choice modelling, statistical modelling, traffic simulation, ML, and optimization.

Responsibilities

• Collect relevant data for model development.
• Design tour-based destination choice models.
• Integrate models into the existing tour-based transport framework.
• Lead funded research projects, supervise students, and prepare proposals and reports.

Skills

Discrete choice modelling

Statistical modelling

Machine learning

Optimization techniques

Programming (Python/Java/C++)

Strong communication

Education

PhD in transport/related disciplines

Tools

EMME

PTV Visum

Job description

Job Title: Research Fellow (Transport Planning)

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Civil and Environmental Engineering

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 11/08/2026

Job Description

We are seeking Research Fellows to join our research team to work with PI and co-PIs in the Department of Civil and Environmental Engineering. The primary focus of this position is on developing destination choice models for future land use and transport systems using data-driven approaches.

Key Responsibilities Include

• Collecting and analyzing relevant data.
• Designing tour-based destination choice models.
• Facilitating developed models into existing tour-based transport framework.
• Leading funded research projects, supervising research students, and preparing research proposals and reports.
  
  

Qualifications

• Possess a PhD in a related discipline (transportation engineering, operations research, computer engineering/science, or related disciplines) by December 2024.
• Strong knowledge and expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques.
• Strong expertise in discrete choice modelling, statistical modelling, and machine learning methods
• Proficiency in transport planning software such as EMME or PTV Visum.
• Programming expertise in at least one language, e.g., Python, Java, or C++.
• Strong written and oral communication skills.
• Open to fixed-term candidate.
  
  

Req ID: 34026