Research Associate (Machine Learning for Earth Observation)
Core
Develop self-supervised, temporal, and multimodal machine learning models for satellite image time series and geospatial data to monitor coastline change, land subsidence, urban expansion, and vegetation.
Role type
Research Associate (Machine Learning for Earth Observation)
Builds
Geospatial foundation models, scalable processing pipelines, and open-source code/datasets
Domain
Earth Observation, Remote Sensing, Machine Learning
Deliverable
production ML models
Required skills
Deep learning, Python, PyTorch, Linux/GPU computing, Remote sensing data processing, Geospatial data analysis, Model evaluation, Open-source code maintenance
Preferred skills
SAR interferometry, Foundation models, Vision-language models, Agentic AI, Generative models (diffusion), PhD research experience
Technologies
Python, PyTorch, Linux, GPU computing, Cloud computing
Responsibilities
Develop deep learning models for satellite image time series and multimodal Earth observation data; Develop next generation geospatial foundation models; Explore emerging methods in machine learning for Earth observation; Apply models to environmental monitoring tasks; Build scalable and reproducible processing pipelines; Prepare data and reference labels, evaluate models; Write papers and reports, present results
Seniority
Mid-level Research Associate (PhD track)