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- Fondazione Bruno Kessler
- Politecnico di Milano
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of data collected from smart bricks and smart mortars using machine learning and artificial intelligence techniques for damage identification and classification, detection of failure mechanisms and attained
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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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include the development of machine learning models and decision-support systems for crop monitoring,early detection of plant stress and diseases, prediction of environmental performance and digital
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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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assurance, data science, data-driven modelling, digital manufacturing workflows, and Digital Product Passport B.3 Hands-on experience in machine learning, ontologies and knowledge graphs, IIoT and digital
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, modelling, or computer-aided design (CAD) skills Specific Requirements Compliance with the MSCA Doctoral Network mobility rule Must not have a doctoral degree at the date of their recruitment Willingness