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the dynamic of OM in the future global climate change by using Soil Organic Models. Mentor master students and PhDs Education, qualifications, and experience Applicants must have an earned doctorate in Agronomy
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. Investigate the interactions between soil microbes and their environment using field and laboratory experiments. Analyze data on soil microbiomes, nutrient cycling, and soil biological processes. Develop models
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(MD) simulations, enabling the computational investigation of material properties, electronic structure, and atomic-scale behavior, particularly in the context of all-solid-state batteries. Showcase
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; Assess the effects of organo-mineral management on soil biological parameters, including soil fauna; Predict the dynamic of OM in the future global climate change by using Soil Organic Models. Mentor
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assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation
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few meters), suitable for plot-scale applications. Surface soil moisture can in turn be used to estimate root zone soil moisture via land surface models that enable linking the surface to root zone
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1400 large container (mini-plots) to study water dynamics and drought scenarios simulation under close-to-field conditions, and (ii) a fully autonomous phenotyping robot, Phenomobile.v2+, equipped with a
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. without vegetation. Studying dust deposition and its impact on energy performance. Analyzing meteorological, soil, and energy production data. Contributing to the modeling of PV performance under local
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Osmosis (FO) — Reverse Osmosis (RO) system. The work of this project includes lab work, computer modelling, life cycle assessment, and techno-economic study. The project will contribute to protecting water
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during natural disasters. AI models can process big datasets efficiently, helping to make informed, unbiased decisions, and ensuring resources are distributed to those most in need. Additionally, when