Research Fellow (Population & Geodemographic Modelling) - Cities Foresight Lab (CFL), NUS Cities

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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 Research Fellow with strong quantitative training and project management skills to lead the development of household archetyping and population geodemographic models for the SGHA-TGM project. The successful candidate will play a key role in designing, implementing, and validating analytical frameworks for household segmentation, synthetic population generation, and town geodemographic projection, working closely with an interdisciplinary team of urban planners, data scientists, and policy partners. This role offers a unique opportunity to work at the intersection of advanced quantitative modelling and real-world urban planning and governance applications.

Responsibilities

. Conceptualise, design and implement household segmentation and archetyping frameworks using socio-demographic, behavioural, and lifestyle data.
. Create synthetic populations that represent households and residents at fine spatial scales.
. Design and implement household geodemographic forecasting models to explore future urban scenarios.
. Conduct model validation, sensitivity analysis, and robustness checks to assess the quality of modelled products.
. Lead and coordinate implementation of the research project.
. Publish research findings in top-tier academic journals and present at local and international conferences.
. Mentor junior research performers and provide active feedback to the team.
. Coordinate, prepare, and present project updates to PIs, Co-PIs, and the funding agency on a regular basis.
. Proven experience in preparing and submitting research ethics applications (e.g., IRB approval) and ensuring compliance throughout the project lifecycle.

Qualifications

. PhD in Economics, Statistics, Data Science, Computer Science or related quantitative disciplines.
. Demonstrable experience in one or more of the following areas: Population modelling Household or individual-level modelling Residential or home location choice modelling Synthetic population generation
. Proficiency in programming languages such as Python and/or R.
. The candidate must be comfortable with synthesis and interpretation of results derived from quantitative and qualitive approaches, as well as a strong ability to integrate multi-source data (government, open-source, community-level).
. Experience in policy-relevant or applied research settings. Prior exposure to urban, housing, or transport modelling contexts.

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)
The anticipated start date for the position is 2 January 2026. We will begin evaluating candidates immediately, but the position will remain open until a suitable candidate is found. Further enquiries should be sent to [Confidential Information].