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

United States Digital Space LLC; Singapore; On-site; SGD 42,000 - 66,000; Full time

Job summary

NUS Cities is seeking a Research Engineer/Assistant to contribute to data science and modelling for the CA-ACM project at Kent Ridge campus. You will work with a multi-disciplinary team spanning urban planning, health, and data science to transform urban behavior theory into data-driven tools.

The role emphasizes hands-on ML development, spatiotemporal data processing with Python and GIS, and producing dashboards, reports, and manuscripts for diverse audiences.

Qualifications

• Master’s or Bachelor’s in a related field.
• Proficiency in Python and ML libraries (scikit-learn, PyTorch/TensorFlow).
• Solid grounding in statistics, probability, and applied mathematics.
• Experience with spatiotemporal data or geospatial analysis is a plus.
• Clear written and verbal communication skills.
• Preferred: Experience with LLM/AI applications, stakeholder-facing work, or research project coordination.

Responsibilities

• Collaborate with the Quantitative Research Fellow to advance data science and modelling components of the CA-ACM project.
• Build and iterate on machine learning models and quantitative methods for activity sequences and mobility patterns.
• Process, label, and visualise spatiotemporal datasets using Python and GIS tools.
• Develop software components using object-oriented programming to support archetype classification, behavioural simulation, and intervention testing.
• Prepare research outputs such as reports, visualisations, dashboards, and academic manuscripts.

Skills

Python programming

Machine learning

GIS/data visualization

Statistics & math

Communication skills

Education

Master's or Bachelor's in Computer Science, Data Science, Urban Analytics, Geoinformatics, Geography

Tools

QGIS

GeoPandas

PostGIS

Scikit-learn

PyTorch/TensorFlow

Job description

Job Title: Research Engineer/Assistant (Urban Analytics & Data Science) - NUS CitiesUniversity-Level Unit: College of Design and EngineeringFaculty/Department-Level Unit: ArchitectureEmployee Category: Research StaffLocation_ONB: Kent Ridge CampusPosting Start Date: 05/08/2026Job 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

You will work closely with the Quantitative Research Fellow to advance the data science and modelling components of the CA-ACM project. This is a hands‑on role where priorities may shift as the research evolves.

Key areas of work include:

• 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.