Research Scientist, Urban Institute

Times Higher Education; Singapore; Sur place; SGD 90 000 - 150 000; Plein temps

Résumé du poste

Singapore Management University (SMU) invites applications for a Research Scientist at the SMU Urban Institute. This role supports interdisciplinary research focused on Asian cities, leveraging data-rich mobility studies in Singapore and beyond.

The Research Scientist will work closely with the PI and Co-PIs to execute projects, building the mobility knowledge graph, modeling shocks and demand, and training AI-based transportation models for scholarly papers and grant proposals.

Qualifications

• PhD in Urban Informatics, Urban Analytics, or related fields.
• Deep knowledge of urban mobility systems and knowledge-graph methods.
• Strong Python programming and data analysis skills.
• Good knowledge of machine learning and its application in urban transportation systems.
• Familiar with large-scale transportation datasets and related spatial-temporal data.

Responsabilités

• Build the Mobility Knowledge graph by pulling together Singapore mobility data into a structured knowledge graph.
• Model shocks and demand on top of the graph to analyze and predict travel demand under different shocks and disruptions.
• Train AI-based transportation models and prepare future research papers and grant proposals.
• Other duties as assigned by PI/Co-PIs.

Connaissances

PhD in Urban Informatics

Urban mobility systems

Python programming

Machine learning

Knowledge-graph methods

Formation

PhD in Urban Informatics or Urban Analytics

Outils

Knowledge-graph tools

Description du poste

Organisation/Company SINGAPORE MANAGEMENT UNIVERSITY Research Field Other Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Application Deadline 14 Nov 2026 - 00:00 (UTC) Country Singapore Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

About Us

Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together - building a strong collegiality and morale within the university.

Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities - all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.

Job Description

The SMU Urban Institute, launched in early 2024, is a university-level research institute dedicated to advancing an interdisciplinary research agenda. Leveraging SMU's strategic position as a dynamic management university in Singapore's city centre, the Institute aims to make a meaningful impact through the study of Asian cities. We are currently seeking individuals with specific skillsets to support the various ongoing research projects at the Institute.

The Research Scientist will work closely with the PI and Co-PIs to execute the project. The job scope involves:

• Build the Mobility Knowledge graph. Pull together Singapore Mobility data into a structured knowledge graph of how the city moves.
• Model shocks and demand on top of it. Use the graph to analyze and predict travel demand under different shocks and disruptions.
• Train AI based transportation model, prepare future research papers and grant proposals.
• Other duties as assigned.

Qualifications

• PhD in Urban Informatics, Urban Analytics, and related fields.
• Have a deep knowledge of urban mobility systems and knowledge-graph methods.
• Strong Python programming and data analysis skills.
• Good knowledge of machine learning and its application in urban transportation systems.
• Familiar with large scale transportation dataset and the related spatial temporal data.