Research Associate (Community Health, Cities and Spatial Analysis)
NATIONAL UNIVERSITY OF SINGAPORE
Singapore, Singapore
Not specified
Quantitative analysis skills
Spatial and geospatial analysis
Hierarchical level modelling expertise
The National University of Singapore is seeking a Research Associate to support research at the intersection of community health, urban environments, and population wellbeing. This role requires expertise in quantitative analysis, spatial methodologies, and collaboration with various stakeholders to inform health policies
Job Summary
This role supports research at the intersection of community health, cities, and population wellbeing through quantitative and spatial analyses.
The successful candidate will analyze place-based initiatives like community gardens and urban design interventions using advanced statistical methods.
Applicants must have at least two years of experience in quantitative, spatial, or computational methods within applied research settings.
Matching Summary
Match Score: 85
The National University of Singapore is seeking a Research Associate to support research at the intersection of community health, urban environments, and population wellbeing. This role requires expertise in quantitative analysis, spatial methodologies, and collaboration with various stakeholders to inform health policies.
Skills & Requirements
Must-have
Quantitative analysis skills
Spatial and geospatial analysis
Hierarchical level modelling expertise
Structural equation modelling
Latent class analysis application
R, Python, or Stata proficiency
Nice-to-have
Experience with large administrative datasets
Background in healthcare economics
Cost-effectiveness analysis experience
Strong analytical writing abilities
Track record of quantitative publications
Key Requirements
Master's degree in Statistics, Economics, Geography, Urban Planning, Public Health, Sociology, Data Science, or related field
At least 2 years of experience in quantitative, spatial, or computational methods
Prior experience working with large datasets, administrative data, or spatial data is an advantage