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the numerical modelling of thermal phenomena, with particular emphasis on solidification processes. d) Knowledge on machine learning methods or data-driven modelling approaches applied to materials science or
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that are only partially met by the development of special purpose classical computing units. This has motivated a recent interest in using quantum computing to machine learning tasks, in particular to clustering
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Intelligence (AI) algorithms, including Machine Learning (ML) and Deep Learning (DL) techniques, for advanced signal analysis. The work will focus on developing methodologies for the detection, extraction
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metal dichalcogenides are promising platforms for next-generation quantum and electronic devices, where atomic defects play a crucial role in determining functionality. This project develops a machine