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or pathological activities are unknown. The aim of this project is to understand how water and protein dynamics is perturbed by LLPS and enable the formation of amyloid assemblies. In particular, we will study the
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Infrastructure? No Offer Description The project's overall aim is to analyze and remove emerging organic micropollutants from water using advanced oxidation processes and mathematical approaches, including
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to characterize colloids and associated TMEs and trace soil-water transfer mechanisms, interpret data, and ultimately communicate results by writing scientific articles and taking part in conferences. The project
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model, NEMO has been designed to run in double precision, i.e. using 64-bit floating-point numbers. Using lower precision can be beneficial in terms of performance by reducing memory usage, execution time
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synergies of irradiation by up to 3 beams on ice composed of simple molecules (H2O, CH3OH, NH3). These results will be compared with the experiments carried out so far with a single irradiation source. Other
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at the Institut de Chimie de Nice as part of the ERC Consolidator Grant ICE-EEVOLVE. This ambitious project aims to investigate the origins and evolution of chiral organic molecules in astrophysical environments
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between surface water and groundwater are influenced by hydro-geomorphological changes. On the Var watershed, physically-based numerical modeling tools (hydrological, hydraulic, and coupled hydrogeological
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their operation. Currently this information is supplied via the Global Carbon Budget (GCB, Friedlingstein et al., 2022), which uses a set of activities known as the ocean carbon value chain to produce an annual
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observed by electron microscopy (SEM, TEM): aqueous solutions, hydrated materials, and interfaces water/solid. This is particularly important for in situ/operando/in vitro conditions as such obtained using
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water. Clearly identified scientific objectives motivate and guide the design and development of space mission by CESBIO and monitoring tools. Missions: - Data processing, apply and tune deep-learning