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innovative experiments. The work will include defining and optimizing in vitro models using diverse cell types and advanced platforms, as well as establishing correlations between applied mechanical stimuli
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alarm protocols; experience in sensor deployment/SCADA and data processing. Ability to optimize regeneration systems and integrate them into service networks; knowledge of EU regulations on water reuse
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Polarization (PHIP). Experience in NMR and MRI pulse sequence development, optimization, and implementation. Strong quantitative and analytical skills for processing and modeling magnetic resonance data
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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