We are the University of Cambridge research centre in Singapore called Cambridge CARES, sponsored by the NRF CREATE program CAM.CREATE. Health-driven design for cities (HD4) is a collaborative research programme between the University of Cambridge, Nanyang Technological University and National University Singapore within Cambridge CARES.

The project focuses on characterising urban environmental exposures across Singapore and linking them to participants in the SG100K cohort. The project assembles data on weather, built environment, population density, transport access and other socio-demographic markers to quantify exposures such as humid heat stress, air pollution, food retail accessibility, land use, amenities and infrastructure. These spatial indicators are then used to describe different types of urban environments that people live in and to support downstream analyses of links between environment, behaviour and health.

Key responsibilities include:

Supporting data collection and consolidation from government and open-source platforms.
Assisting with geospatial processing.
Helping to develop exposure measures.
Contributing to data-driven models that classify neighbourhood types and characterise environmental exposures across Singapore.
Preparing clear documentation, summary tables, and basic visualisations for internal reports and collaboration with other HD4 work packages.

The intern will work within a multidisciplinary team at Cambridge CARES, contributing to a Singapore-wide environmental data infrastructure that supports health, urban planning, and policy-relevant research.

Internship requirements:

Currently enrolled in a Bachelor’s (senior year) or Master’s programme in: Geography, Urban/Regional Planning, Data Science, Environmental Science/Environmental Engineering, or related fields.
Comfortable working with data from multiple sources, including messy or incomplete datasets.
Good written and verbal communication skills in English; able to document workflows and collaborate with a diverse team.
Experience with GIS tools (e.g. QGIS, ArcGIS) and/or geospatial Python/R libraries.
Familiarity with remote sensing and environmental indicators (e.g. land cover, NDVI, air quality indices, climate data).
Basic understanding of statistical modelling or clustering (e.g. regression, unsupervised learning) for classifying environmental types.
Experience working with large datasets or reproducible workflows.
Strong curiosity about how cities affect health and how data can inform better urban design and policy.
Detail-oriented and organised in handling data and code.
Willingness to learn new tools and concepts independently and to seek feedback proactively.

Skills gained by the end of the internsip:

By the end of the internship, the intern is expected to gain:

Hands-on experience in geospatial data engineering, including assembling and harmonising multi-source urban and environmental datasets at city scale.
Practical skills in GIS and spatial analytics.
Experience constructing urban exposure metrics.
Exposure to data-driven methods for classifying neighbourhood types and describing variation in exposures across socio-demographic groups.
Improved proficiency in Python/R for data science, including handling large datasets, producing clear visualisations and basic statistical summaries.
Understanding of how city-scale environmental datasets can be linked to cohort/health data to support research on environment–health relationships and to inform urban and health policy.
Experience working in a multidisciplinary, international research environment at the interface of urban design, health, and data science.

Please note this post is mainly based in the CREATE Tower at NUS University Town, Singapore.

Please apply by uploading your CV, academic transcript (unofficial is acceptable) and a short statement of interest (max 1 page) describing relevant experience and motivation.

If you have any questions, please feel free to reach out to the HR team at recruitment@cares.cam.ac.uk

Cambridge CARES

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Research Intern – Urban Environmental Exposures & Geospatial Analytics