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nanocrystals, hybrid perovskites and 2D materials. Development of new data-driven approaches for studies of optoelectronic properties using EM, including machine learning / machine vision algorithms. The balance
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analytical methods for modeling and simulation in the Urban Digital Twin; second, assessing the validation of the proposed framework on real-world scenarios concerning the adoption of Digital Twin by Italian
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. Using controlled in vitro exposure experiments combined with high-resolution analytical techniques (HPLC, ICP-MS, Seahorse XF), the PhD student will characterize MN kinetics, dose-response relationships
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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
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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