Job Description

•    Cities Foresight Lab (CFL) is a growing multi-disciplinary research group at NUS building deep S&T capabilities at the intersection of urban planning, policy 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 and talented Research Fellow to lead the design and implementation of data engineering infrastructure and large-scale modelling of the CA-ACM project.

Responsibilities 
•    Develop the data infrastructure and modelling pipelines
•    Design and implement scalable ETL workflows to integrate large-scale spatiotemporal, behavioural, and health-related datasets.
•    Perform data fusion and feature engineering on diverse, multi-modal data sources (e.g., wearables, spatiotemporal, built environment data)
•    Design and implement advanced statistical and/or ML models to derive individual archetypes and surface latent patterns from large-scale multi-modal datasets (e.g., spatiotemporal mobility chain, wearables, geospatial, survey).
•    Design and implement sequence prediction and simulation models, including but not limited to Markov and/or Choice model variants.
•    Collaborate with a team of qualitative and quantitative researchers to achieve overall project goals; supervise and mentor Research Assistants
•    Contribute to research synthesis, writing publications and presenting findings at academic conferences, workshops, and stakeholder meetings.

Qualifications

•    A PhD in Quantitative fields such as Computational Social Science, Transportation Engineering, Computer Science/Data Science, or other related disciplines.
•    Demonstrable expertise in predictive modelling of human behaviour and mobility using advanced statistical and ML techniques. Experience with agent-based modelling (ABM) is preferred.
•    Proficiency in building data and modelling pipelines using Python (e.g., Pandas, Geopandas, Scikit-Learn, PyTorch/TensorFlow). Knowledge of other programming languages is a plus.
•    Experience working with large-scale multi-modal datasets.
•    Proven track record of research excellence, demonstrated through publications in reputable journals and conferences.
•    Experience working in interdisciplinary research teams and collaborating across diverse fields.
•    Excellent project management and organizational skills.
•    Strong communication skills, with the ability to convey complex ideas to both academic and non-academic audiences.
•    Ability to work independently and collaboratively in a fast-paced research environment.

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 four referees (including applicant’s main PhD supervisor). Only short-listed applicants will be invited to submit reference letters.

The anticipated start date for the position is 1 June 2026. We will begin evaluating candidates immediately, but the position will remain open until a suitable candidate is found. E-mail applications and further enquiries should be sent to nixiesap@nus.edu.sg (please indicate “Research Fellow (Human Mobility & Behavioural Modeler) Application for CA-ACM” as the subject heading).

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : Architecture

Employee Referral Eligible: No

Job requisition ID : 32033