Research Associate (Spatial Environmental Health and AI-based Exposure Modelling)
In this rapidly changing world, the Centre for Climate Change and Environmental Health (CCEH) is advancing research on how climate change, air pollution, and extreme environmental conditions affect human health. By bringing together expertise across environmental science, exposure assessment, epidemiology, and public health, CCEH develops evidence-based solutions to support healthier and more climate-resilient communities in Singapore and across Asia.
Within CCEH, the Environmental Health team, led by Prof. Steve Yim, investigates the interactions between climate, air quality, and human health using integrated measurement, modelling, and statistical approaches. The team works across multiple spatial scales to understand how environmental exposures affect human health and to generate scientific evidence for public health policy and workforce protection strategies.
Key Responsibilities:
The position will support research on environmental exposure assessment and its implications for human health, with a focus on climate- and environment-related risks such as heat, air pollution, temperature variability, and urban environmental factors. The role will contribute to the collection, integration, analysis, and interpretation of climate, environmental, land-use, remote sensing, socioeconomic, and health-related data. The successful candidate will apply spatial analysis, artificial intelligence, machine learning, land-use regression, and spatiotemporal modelling approaches to develop exposure estimates, generate maps and indicators, support research publications and reports, and collaborate with research partners to strengthen the Centre’s evidence base for environmental health, climate adaptation, and public health policy.
Job Requirements:
Have a Master’s degree in Geographic Information Science, geospatial science, geography, environmental health, public health, epidemiology, environmental science, data science, climate science, or a related discipline.
Have research experience in environmental exposure assessment, spatial analysis, land-use regression modelling, environmental epidemiology, climate change and health, air pollution, heat exposure, or related fields.
Have experience in analyzing climate, environmental, land-use, remote sensing, health, or socioeconomic data.
Have experience using R, Python, GIS software, Google Earth Engine, or other relevant statistical and geospatial analytical tools.
Have good organizational, communication, and academic writing skills, with the ability to work independently and collaboratively in interdisciplinary teams.
Candidates with relevant publications, project experience, or experience applying machine learning or artificial intelligence techniques in environmental health research will be advantageous.
We regret that only shortlisted candidates will be notified.
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