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-mantle NCMASF3 system; ii) Training a Mixture Density Network emulator on 106 thermodynamic evaluations; iii) Implementing a global MCMC Bayesian inversion of the SPARTANS tomographic model; and iv
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finite-temperature anharmonic effects, thereby expanding the existing ab initio thermodynamic database to support the Bayesian inversion framework of the SHARP Thematic Project (Task 21, WP3). The project
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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management databases; applying statistical methods to identify the main factors driving yield gaps; estimating productivity frontiers and crop responses to management practices; developing agronomic