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strategies and artificial intelligence based on chemical analysis results. The researcher will be responsible for the integration, processing, and interpretation of data, applying statistical methods and
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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, generalized transverse-momentum-dependent distributions (GTMDs), and electromagnetic and gravitational form factors, using continuum dynamical methods. Mandatory requirements: Ph.D. in Nuclear Physics, Hadron
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microscopy (FM), electron microscopy data analysis, nanoscale structural characterization, or the development of computational methods and tools for materials characterization will also be valued. How to apply
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of phytohormones produced by microalgae. Activities will include microalgal cultivation and strain selection, optimization of metabolite extraction and purification methods, chromatographic analyses using HPLC and
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recipient will contribute to the core research theme of the RATIONAL project, which focuses on investigating and developing robust evidence-based retrieval methods for Natural Language Inference (NLI) tasks
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. Mandatory requirements: PhD in Social Sciences or a related field, completed less than seven years ago, subject to extensions allowed by FAPESP; proficiency in qualitative methods and basic knowledge
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institution; oral and written fluency in Portuguese and English; a consistent record of peer-reviewed publications over the past 5 years; experience in research and qualitative methods; availability to live in
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potentials (DeePMD-kit, NequIP/Allegro, or similar). Familiarity with cluster-expansion/Monte Carlo methods, thermodynamic integration, or elastic-constant calculations. Knowledge of high-pressure mineral
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, and machine learning applications for developing predictive methods. Desirable requirements: The candidate must possess scientific maturity to solve problems, proficiency in English scientific