PhD position in High-Resolution Mapping Fuel Loads, Fuel Moisture and Microclimates for Next-Generation Wildfire Risk Assessment
Core
Develop near-real-time, high-resolution maps of fuel loads, fuel moisture, and microclimate conditions to support wildfire risk assessment and management in Flanders, Belgium.
Role type
PhD researcher in ecological modelling and remote sensing
Builds
High-resolution fire weather products and decision-support tools for land managers and policymakers
Domain
Environmental science, remote sensing, and wildfire risk management
Deliverable
production ML models | dashboards & analysis
Required skills
Ecological modelling, remote sensing, spatial data analysis, deep learning (CNNs), machine learning, programming in R, field ecology
Preferred skills
UAV operation, experience with LiDAR systems, knowledge of Sentinel-1/2 imagery, familiarity with multimodal foundation models for Earth observation
Technologies
R, CNNs, Sentinel-1, Sentinel-2, LiDAR, UAVs, high-performance computing
Responsibilities
Develop methods to map fuel loads using airborne/UAV LiDAR, field inventories, and satellite imagery; Quantify and predict live fuel moisture content combining field measurements with radar/optical imagery and ML; Model microclimate conditions using logger networks, UAV data, and satellite imagery to predict fire weather indices
Seniority
PhD candidate, research-focused