-
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
-
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
-
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
-
integrating AI with vision at the edge. Despite recent advancements, the synergy between AI and computer vision remains constrained by fundamental imaging bottlenecks. Conventional HDR techniques frequently
-
Job related to staff position within a Research Infrastructure? No Offer Description The design process of complex systems must guarantee not only the functional correctness of the implemented system
-
process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices
-
of the city, including the effects and impacts of external changes (eg, climate change) and internal processes (eg, urban transition policies and incentives). A challenge for the development of Urban Digital
-
Job related to staff position within a Research Infrastructure? No Offer Description How we measure and define performance shapes the systems we build. Current Natural Language Processing (NLP
-
of artificial intelligence across public administrations and enterprises. Its impact, however, will depend on how legal obligations are translated into organizational processes, procurement strategies, and