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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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physical effects. The research activity will also involve the study, simulation, development, and experimental validation of signal-processing techniques and electronic circuits, with particular attention
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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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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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spectroscopic signals in scientific payloads for astrophysics, planetary exploration, and satellite-based earth observation. Space missions such as ISO, Herschel, Webb, and SPHEREx demonstrate the continued
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of advanced OCT acquisition and reconstruction methods Signal processing and compressed sensing approaches for dynamic imaging AI- and deep-learning-based image enhancement and artifact suppression Experimental