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.

• →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.
• →Build scalable and reproducible processing pipelines on high-performance and cloud computing and maintain open-source code and datasets.
• →Prepare data and reference labels, evaluate models and maintain open-source code.
• →Write papers and reports, present results and support the research group.

• Master's degree in computer science, electrical engineering, remote sensing, geomatics, civil and environmental engineering or a related field.
• Knowledge of machine learning and deep learning.
• Experience with remote sensing or geospatial data; knowledge of SAR interferometry is a plus.
• Proficiency in Python and PyTorch on Linux/GPU computing; experience with foundation models, vision–language models or AI agents is a plus.
• Good communication skills; able to work independently and in a team.
• Self-driven and interested in pursuing a PhD.

We regret that only shortlisted candidates will be notified.