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Nanyang Technological University

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Research Associate in Machine Learning for Earth Observation Nanyang Technological University in Singapore

Degree Level

not provided

Field of study

Computer Science

Funding

Available

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Country

Singapore

University

Nanyang Technological University

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Keywords

Computer Science
Environmental Science
Deep Learning
Remote Sensing
Civil Engineering
Earth Science
Earth Observation
Generative Modeling
Geospatial Information
ML

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About this position

Nanyang Technological University’s School of Civil and Environmental Engineering is seeking a Research Associate to work on machine learning for Earth observation. The project focuses on developing self-supervised, temporal and multimodal models for satellite image time series and geospatial data.

The research will investigate next-generation geospatial foundation models and emerging artificial intelligence methods, including vision–language models, multimodal large language models, agentic AI workflows and generative models such as diffusion models. These approaches will connect satellite data with text, support automated geospatial analysis and enable data synthesis.

The models will be applied to important environmental and urban monitoring problems, including coastline change, land subsidence, urban expansion and vegetation monitoring. The researcher will prepare data and reference labels, evaluate models, develop scalable and reproducible processing pipelines, and work with high-performance and cloud computing infrastructure.

The role also includes maintaining open-source code and datasets, writing research papers and reports, presenting results and supporting the research group. The successful researcher will benefit from access to high-performance GPU computing and close supervision within NTU’s research-intensive environment.

Funding details

Available

What's required

A Master's degree in computer science, electrical engineering, remote sensing, geomatics, civil and environmental engineering, or a related field is required. Applicants should have knowledge of machine learning and deep learning, experience with remote sensing or geospatial data, and proficiency in Python and PyTorch on Linux/GPU computing. Knowledge of SAR interferometry, foundation models, vision-language models, or AI agents is advantageous. Good communication skills, the ability to work independently and in a team, and a self-driven attitude are expected. Interest in pursuing a PhD is desirable.

How to apply

Apply through the NTU Workday application portal using the Research Associate (Machine Learning for Earth Observation) vacancy. Submit the application materials requested in the portal.

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