In A Nutshell
Position focuses on leveraging EO data and causal machine learning to systematically uncover the drivers of urban flooding and quantify their impacts on flood resilience.
Responsibilities
- Build a comprehensive multi-modal urban flood dataset integrating Sentinel-1/2, PlanetScope and other data sources.
- Develop robust AI models for urban flood mapping using sparse multi-temporal, multi-source EO data (SAR intensity, InSAR coherence, optical), including cross-modal fusion and modality distillation.
- Design a causation analysis framework combining deep learning with causal discovery & inference to quantify the influence of rainfall, drainage capacity, and 3D urban form on flood severity.
- Validate results with hydrodynamic simulations and 3D urban semantic models, benchmark against state-of-the-art methods, and publish in leading international journals and conferences.
- Literature research.
- Scientific publishing.
Skillset
- Completed academic university degree (university diploma / M.Sc.) in Computer Science, Geoscience, Remote Sensing, Hydrology, Data Science, Physics, or related fields.
- Experience in machine learning (ML), artificial intelligence (AI) or related fields.
- Software skills in ML languages such as Python.
- Ability and enthusiasm to learn new technologies quickly.
- Ability to work highly motivated both independently and in a team.
- Very good written and spoken English skills.
- Some knowledge or background in the SAR is an advantage.
- Knowledge of causal ML, graphical models, or multimodal data fusion is an advantage.