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Identification of Objects in Massive, Multi-scale, and Temporal 3D Point Clouds Objectives: The MODAM3 project aims to develop new models and algorithms for the automatic localization, characterization, and
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degree in information and communication engineering, with research experience in 3D point-cloud or point-cloud sequence processing, is highly desirable. The candidate should posse extensive relevant
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related field A strong background in machine learning, computer vision, or 3D point cloud processing, as well as in probabilistic modelling and Bayesian inference Very good programming skills, preferably in
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are still present. This PhD research aims to develop new and efficient AI-based solutions for processing LiDAR data (2D/raster and 3D/point clouds) and improve the detection of sub-canopy archaeological
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a strong background in artificial intelligence and 3D computer vision, with solid programming skills and an interest in 3D data processing (e.g. point clouds and neural scene representations), along
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Change Adaptation (CCA) simulations and activities in urban environments. The PhD project should leverage multi-scale geospatial data (remote sensing/aerial imagery, point clouds, maps, etc.) and GeoAI
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software development for robotics or mobile sensing applications. Familiarity with Linux-based development, ROS, sensor integration, real-time data processing, point-cloud processing, 3D reconstruction
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of objects for the real-time segmentation of point clouds. Experiencia demostrable en el manejo de técnicas de aprendizaje automático (ML/DL) aplicado a imágenes. / Proven experience in the use of machine
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AI systems for real-time 3D mapping on compact, low-power devices. The project will combine optical sensing, event-based vision, and radio-frequency (RF) data with advanced AI to build robust mapping
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, Python, and C coding; signal processing and filtering; CAD software for mechanical design and 3D printing; AI tools including Claude, Codex, or Cursor. Human Subjects & Safety: Basic understanding of human