Research Associate (Machine Learning for Earth Observation)

The School of Civil and Environmental Engineering (CEE) at NTU, established in 1981, aims to be a world-leading school in sustainability and the built environment, addressing urbanisation and climate change through research and innovation. Do you want to use the latest artificial intelligence to understand our changing planet? We are looking for a Research Associate in Machine Learning for Earth Observation to develop self-supervised, temporal and multimodal models for satellite image time series and geospatial data.

The models will be applied to coastline change, land subsidence, urban expansion and vegetation monitoring. The Research Associate will also have the opportunity to explore emerging directions in machine learning for Earth observation, such as vision–language models, agentic AI and generative models. We provide access to high-performance GPU computing as well as close supervision.

Key

Develop deep learning models for satellite image time series and multimodal Earth observation data. Develop next generation geospatial foundation models, such as carbon minimization. Explore emerging methods in machine learning for Earth observation, such as vision–language and multimodal large language models that link satellite data with text, agentic AI workflows that plan and carry out geospatial analyses, and generative (e.g.

diffusion) models for data synthesis. Apply the models to coastline change, land subsidence, urban expansion and vegetation monitoring.