National University Of Singapore Hiring! Full Time Research Engineer-Assistant (Urban Analytics - Data Science) - NUS Cities in Central Region (Singapore) - Ricebowl

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
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.

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.

We will begin evaluating candidates immediately, but the position will remain open until a suitable candidate is found. Further enquiries can be sent to ************* (please indicate “Research Assistant (Urban Analytics & Data Science) Application for CA-ACM”).