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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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. The goal of this PhD Thesis is to bridge these dimensions, moving beyond rigid profiles to develop adaptive, context-sensitive persona models. By integrating deep profile characteristics with real-time
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develop evaluation frameworks that capture not only overall system performance, but also real-world utility, fairness, and responsiveness to the needs of diverse stakeholders. Where to apply Website https
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, and the integration of compliance requirements into decision-making structures. It will combine doctrinal legal analysis with case studies and design-oriented methodologies in order to develop
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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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and simulation techniques are necessary to develop Urban Digital Twins able to manage this complexity and produce reliable predictions. The candidate will be requested to contribute to this research
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of the proposed PhD are: (i) To investigate novel neural scene representation methods and their complementary with respect to conventional geometric methods (ii) To develop methods to decompose raw 3D scenes
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look for correlations between transient phenomena in the ionosphere and seismic events. The successful candidate will develop and apply state-of-the-art machine learning techniques to enhance
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the use of formal monitors and safety cages to provide dependability guarantees for components using AI. By building on state-of-the-art tools like OCRA and xSAP, the goal is to develop a robust MBSE flow
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the structural and electronic properties of complex materials. Using first-principles simulations, machine learning techniques, and advanced Monte Carlo methods, the student will develop predictive