New

PhD position on Causal Machine Learning & Earth Observation for Urban Flood Resilience

Full-time

On Site

Deadline

November 1, 2025

About the organization

TUM logo

Technical University of Munich

Organization type

Academic Institution

In A Nutshell

Location

On Site Munich, Germany

Job Type

Full-time

Experience Level

Entry-level

Deadline to apply

November 1, 2025

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.

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