Research Fellow (Early Warning System)

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, public health, and predictive modelling, CCEH develops evidence-based solutions to support healthier and more climate-resilient communities in Singapore and across Asia. Within CCEH, the Climate and AI team, led by Prof. Steve Yim, investigates the interactions between climate, air quality, environmental hazards, and human health using integrated observations, forecasting systems, artificial intelligence, and statistical approaches. The team works across multiple spatial and temporal scales to improve the prediction of environmental health risks and to generate scientific evidence that supports public health policy, workforce protection, and climate adaptation strategies. Key Responsibilities : The position will support the development of environmental health early warning systems and predictive models for climate-sensitive hazards and their impacts on human health. The role will contribute to research on the forecasting and risk assessment of extreme heat, humid heat stress, air pollution episodes, transboundary haze, extreme weather events, and other climate-related environmental hazards across Singapore and Southeast Asia. The successful candidate will analyse and integrate climate, environmental, health, and socioeconomic datasets using advanced statistical, spatiotemporal, and artificial intelligence methods; develop and evaluate hazard forecasting and health-risk prediction frameworks; contribute to research publications, technical reports, and decision-support tools; and collaborate with research partners and stakeholders to strengthen early warning capabilities, climate services, and evidence-based adaptation planning. Job Requirements : Have a Ph.D. degree in atmospheric science, climate science, environmental health, environmental science, computer science, artificial intelligence, data science, public health, epidemiology, geography, or another related discipline. Have strong research experience in artificial intelligence, machine learning, deep learning, environmental modelling, climate-health research, early warning systems, or related fields. Have strong programming and computational skills in Python, R, MATLAB, or related analytical and machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). Have good organizational and communication skills and evidence of the ability to work independently as well as collaboratively across interdisciplinary and multi-institutional teams. Evidence of excellent report writing skills, and the ability to contribute to scientific publications and other research outputs. We regret that only shortlisted candidates will be notified.