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, implementation, and validation of architectures and methodologies for efficient and distributed data processing along the Edge-Cloud-HPC continuum, through the integration of Artificial Intelligence techniques
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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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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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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Digital infrastructures – cloud computing networks, broadband systems, operating
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simulation, Digital Twins, Big Data, IoT/Web of Things, HCI, Edge/Cloud Computing, AI, Computer Vision, Machine/Transfer Learning, Computer Science Education, Computational Thinking, Formal Methods, Logic, Web
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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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integrated into edge-cloud digital platforms, with a focus on sustainability Where to apply Website http://www.unict.it Requirements Additional Information Eligibility criteria Eligible destination country/ies