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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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transformations, providing sustained growth by way of fertilizer production. Notwithstanding, due to high functioning temperature and pressure, this 'simple' reaction accounts for greater than 1-2% of our annual
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produce high-quality scientific articles of which they are the first author, to disseminate their results. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6226-JAMBEH-003/Default.aspx
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applications Improving model architectures, prompting strategies, and retrieval systems for high-stakes environments Conducting pilot evaluations with judicial partners to study how AI tools perform in real
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of polyploid genome evolution across contrasting timescales. The student will receive interdisciplinary training in bioinformatics, evolutionary genomics, and high-performance computing within the DDLS data
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English. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high-performance and cloud computing; (iii
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, i.e., high-fidelity virtual representations of agricultural ecosystems. These digital twins will enhance our understanding of ecosystem functioning, more specifically water and carbon fluxes, supporting
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well as their catalytic performance (activity, recyclability, etc.). Ultimately, if these materials demonstrate high catalytic performance in terms of conversion yield, selectivity and reaction kinetics, they could be used
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physical modelling to better understand the physical and chemical phenomena underlying plasmonic activity, with the goal of developing high-performance materials. In this context, the PhD student will be
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management Background: materials science, mechanical engineering, or a closely related discipline Apply: https://mgician.eu/research/doctoral-candidate-projects/dc6/ DC7: High-Performance Magnesium-Based