Research Engineer/Assistant (Urban Analytics & Data Science) - NUS Cities

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

The National University of Singapore (NUS) invites applications for a Research Engineer/Assistant to contribute to the CA-ACM project within NUS Cities. You will work on data science and modelling tasks at the intersection of urban planning, governance, and strategic insight.

The role focuses on translating behavioural theory into data-driven tools. Applicants should have strong Python skills, ML library experience, and a background in geospatial analysis.

Qualifications

• Degree in CS/Data Science/Urban Analytics/Geoinformatics/Geography or related field.
• Proficiency in Python and ML libraries (scikit-learn, PyTorch, TensorFlow).
• Strong grounding in statistics and applied mathematics.
• Experience with spatiotemporal data or geospatial analysis is a plus.

Responsibilities

• Build and iterate ML models for activity sequences and mobility patterns.
• Process, label, and visualize spatiotemporal datasets using Python and GIS tools.
• Develop software components to support archetype classification and behavioural simulation.
• Prepare research outputs and present findings to diverse audiences.

Skills

Python

ML libraries

Geospatial analysis

Data science

Communication

Education

Bachelor's or Master's in CS/Data Science/Urban Analytics/Geoinformatics/Geography

Tools

QGIS

GeoPandas

PostGIS

Job description

Company description

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

Job Description

NUS Cities is a university-wide, interdisciplinary entity hosted within the College of Design and Engineering, serving as an open and inclusive collaborative platform spanning Education, Research, and Advisory Services. The Cities Foresight Lab (CFL) is a growing multi-disciplinary research group at NUS Cities, operating at the intersection of urban planning, governance, and strategic insight.

The Community Assets and Activity Chain Modelling (CA-ACM) project is a research study commissioned by the Health Promotion Board to investigate how Singapore's built environment shapes residents' daily activities and lifestyle patterns. The project aims to identify features of the built environment that make active living intuitive and natural; develop composite indicators to measure and rank the attractiveness of different urban settings for various population groups; and uncover how these environmental features influence the type of physical activities people choose to engage in. The project brings together experts in urban studies, data science, public health, and social science research to surface evidence-based insights and design strategies that promote more active living.

We are seeking a highly motivated Research Engineer/Assistant to contribute to the data science and modelling components of the CA-ACM project.

Responsibilities

• Build and iterate on machine learning models and quantitative methods — particularly around activity sequences, mobility patterns, and resident archetyping — translating urban behavioural theory into practical, data-driven tools.
• Process, label, and visualise spatiotemporal and built environment datasets using Python and GIS tools (e.g., QGIS, GeoPandas, PostGIS).
• Develop software components using object-oriented programming to support archetype classification, behavioural simulation, and intervention testing.
• Help prepare research outputs such as reports, visualisations, dashboards, and academic manuscripts, and present findings to varied audiences.

Qualifications

• Master's or Bachelor's degree in Computer Science, Data Science, Urban Analytics, Geoinformatics, Geography, or a related field.
• Proficiency in Python and familiarity with ML libraries (e.g., scikit-learn, PyTorch/TensorFlow).
• Solid grounding in statistics, probability, and applied mathematics, with strong attention to detail.
• Experience with spatiotemporal data, sequence modelling (e.g., trip chains), or geospatial analysis is a strong plus.
• Comfortable working flexibly across tasks in a dynamic, interdisciplinary research environment.
• Clear written and verbal communication skills.
• Preferred: Experience with LLM/AI applications, stakeholder-facing work, or research project coordination.