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architectures for AI vision at the edge. AI4IV is offering a PhD scholarship jointly with FBK. The objective is to develop high-efficiency, silicon-native solutions that bridge the gap between neuromorphic
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archaeological signatures (e.g., micro-relief, edge structures, etc.) – Design and implementation of new deep learning architectures (both supervised and unsupervised/few-shot, 2D and 3D) for an efficient and
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sensors. The architecture integrates (i) an ultra-low form factor microfluidic separation module and (ii) a multi-modal SiC sensor for precise analyte identification. The seamless integration of a
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, inconsistencies, or potential deviations from evidence-based recommendations. From a technical perspective, the project will explore alternative architectural solutions, including modular and agentic pipelines
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-based design to manage architectural complexity, automated generation of safety artifacts (such as Fault Trees and FMEA), and formal verification of FDIR strategies. Furthermore, the research will explore