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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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-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
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–microfluidic platforms capable of generating and manipulating synthetic chiral light Explore novel photonic architectures and materials for enhanced light confinement and sensing performance Collaborate with
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, and a Continuum+K3 GIF. The group will also further data analysis using and developing software routines for analysis of large datasets. Research projects span beam-sensitive materials with
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of the software and its configurations. This project will investigate novel techniques for the application of formal methods to the design, verification, and validation of embedded systems, with particular emphasis
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-twin concepts, and programming (e.g. Python, MATLAB, LabVIEW or similar engineering software) B.4 Strong interest in metrology B.5 Experience in working with experimental setups, laboratory equipment
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physics, photonics, biomedical engineering, electronic engineering, computer science/software engineering, or related fields (by September 2026) Strong programming and analytical skills (MATLAB, LabView
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immunostainer, Leica AT2 and Leica CS2 Slide Scanners, for Whole Slide Imaging, Zeiss Axio Imager 2 microscope equipped with Apotome.2 structured illumination and HALO (Indica Labs) software for advanced image