Research Assistant (Data Science & Geodemographic Projection)-Cities Foresight Lab(CFL),NUS Cities

Job Title: Research Assistant (Data Science & Geodemographic Projection)-Cities Foresight Lab(CFL),NUS Cities University-Level Unit: College of Design and Engineering Faculty/Department-Level Unit: Architecture Employee Category: Research Staff Location_ONB: Kent Ridge Campus Posting Start Date: 26/03/2026 Job Description

The Singapore Household Archetyping & Town Geodemographic Modelling (SGHA-TGM) project, commissioned by the Ministry of National Development, aims to build foundational knowledge in Singapore population geodemographics, with a particular emphasis on understanding household lifestyle preferences and their relationships with the built environment. The project is intended to develop the knowledge and technical capabilities to surface insights into the diverse socio-spatial contexts of urban living in Singapore, to support urban planners in anticipating evolving needs, sensemaking sentiments on the ground, and fostering a more inclusive and liveable urban environment.

We seek a highly motivated Research Assistant to contribute to the quantitative components of the SGHA-TGM project. You will develop computational models to enable the discovery of latent population structures, archetypes, and typologies from large-scale datasets. You will work alongside qualitative researchers to interpret and evolve the representational fidelity of groupings and insights derived from data to connect to lived experience and reflect real diversity in urban social life. These outputs will support forecasting frameworks and help planners anticipate evolving household needs at finer spatial scales.

Responsibilities

• Clean, process, and integrate large population datasets from multiple sources (demographics, survey, behavioral, or administrative data), with differing schemas and granularity.
• Implement clustering, classification, scenario simulation/projection, and probabilistic modeling techniques (such as statistical matching, spatial disaggregation, generative models) to derive household/resident archetypes from heterogeneous data sources.
• Prototype, test, and refine different models/algorithms.
• Collaborate with social science & urban studies researchers to ground quantitative findings in real-world population dynamics.
• Coordinate with other technical teams to align modeling outputs with downstream projection needs and improve end-to-end workflows.
• Contribute to data/method documentation, visualisations, and writing reports/publications.

Qualifications

• Bachelor’s or Master’s Degree in Data Science, Statistics, Computer Science, Urban Analytics, or related quantitative disciplines.
• Proficiency in Python and/or R, including data manipulation and analysis packages (e.g. SPSS/SAS).
• Experience with clustering, classification, and supervised ML techniques, and relevant ML libraries (e.g., sklearn, PyTorch)
• Familiarity with working on large and multi-source datasets; both structured and unstructured.
• Resourceful and critical with good communication skills; able to work independently while collaborating effectively with interdisciplinary teams of social scientist and planners.

Preferred

• Experience working with population data (e.g., census), as well as behavioural (e.g., travel survey, choice experiments) and/or spatial data structures.
• Familiarity with probabilistic models, generative models, graphical models, and/or deep learning.
• Familiarity with data fusion techniques such as statistical matching and/or marginal fitting.
• Interest in social/urban applications of data science.

Application Procedure

Interested applicants should submit a dossier consisting of the following:

• a cover letter (maximum 3 pages)
• up-to-date CV
• a statement describing their research trajectory, interests and career ambitions
• contact details for three referees. Only short-listed applicants will be invited to submit reference letters.