Research Associate (Machine Learning for Earth Observation)

Nanyang Technological University; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

NTU School of Civil and Environmental Engineering seeks a Research Associate in Machine Learning for Earth Observation to build self-supervised, temporal, and multimodal models for satellite image time series and geospatial data. The role involves exploring vision–language models, AI agents, and generative approaches, with access to GPU computing and close supervision.

The successful candidate will apply methods to coastline change, land subsidence, urban expansion and vegetation monitoring,

Qualifications

• Master's degree in a related field.
• Knowledge of machine learning and deep learning.
• Experience with remote sensing or geospatial data; SAR interferometry is a plus.
• Proficiency in Python and PyTorch on Linux/GPU computing; knowledge of 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.

Responsibilities

• 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 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.

Skills

Machine learning & deep learning

Communication

Independent work

PhD pursuit

Education

Master's degree in CS/EE/Remote Sensing/Geomatics/Civil & Environmental Eng or related field

Tools

Python

PyTorch

Job description

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 Responsibilities:

• 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.

Job Requirements:

• 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.

Hiring Institution:

NTU A research-intensive public university, Nanyang Technological University, Singapore (NTU Singapore) has 33,000 undergraduate and postgraduate students in the Engineering, Business, Science, Humanities, Arts, & Social Sciences, and Graduate colleges. It also has a medical school, the Lee Kong Chian School of Medicine, established jointly with Imperial College London. NTU is also home to world-class autonomous institutes – the National Institute of Education, S Rajaratnam School of International Studies, Earth Observatory of Singapore, and Singapore Centre for Environmental Life Sciences Engineering – and various leading research centres such as the Nanyang Environment & Water Research Institute (NEWRI) and Energy Research Institute @ NTU (ERI@N). Ranked amongst the world’s top universities by QS, NTU has also been named the world’s top young university for the past seven years. The University’s main campus is frequently listed among the Top 15 most beautiful university campuses in the world and has 57 Green Mark-certified (equivalent to LEED-certified) buildings, of which 95% are certified Green Mark Platinum. Apart from its main campus, NTU also has a campus in Novena, Singapore’s healthcare district. Under the NTU Smart Campus vision, the University harnesses the power of digital technology and tech-enabled solutions to support better learning and living experiences, the discovery of new knowledge, and the sustainability of resources. For more information, visit www.ntu.edu.sg About NIE About RSIS About LKCMedicine About NTUItive