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interface between ESM and the latest version of SIESTA, its validation, its benchmarking, and its exploitation in scientific applications. Work will also involve the optimization of SIESTA on the fastest and
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Leonardo. The successful candidate will play a crucial role in developing and optimizing machine learning workflows for large-scale environmental data analysis, contributing to the creation of robust and
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