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degrees of risk-taking, coexist to optimize resource exploitation while reducing the level of competition among conspecifics. Although the ability to imitate and even cooperate is well documented in many
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that these different strategies, involving varying degrees of risk-taking, coexist to optimize resource exploitation while reducing the level of competition among conspecifics. Although the ability to imitate and even
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for their functions in PDE, or co-precipitation with known PDE factors, which could not recover transient and/or unstable interactors. To overcome these limitations, this project aims to a) optimize Cas13-based gene
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to rationally design new supported activation systems; • Understanding and optimizing the process of metallocene activation, with the aim of developing more efficient polymerization catalysts. This research topic
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requires fundamental and applied research for their optimization, better understanding and industrialization. The project aims to develop and characterize new “reactive” hydrophilic and lipophilic DES
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, extinctions, and environmental change; ● Running simulations and scenario analyses to explore how different discounting rules or time preferences shift optimal conservation choices; ● Fitting models
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will be leveraged to improve existing modulation models describing how large scales alter heat transfer. Optimal oscillations will be designed using reinforcement learning. Extending inner-outer
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of these materials and structures. This approach enhances both predictive simulation and inverse design strategies, optimizing the composition and arrangement of materials in the 3D design space. Within
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using imaging and behavior platforms at SPPIN. This project will allow the control of MeCP2 expression in order to optimize the efficacy of RTT gene therapy, while establishing a modular framework for
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(zeolite structure, texture, composition) that optimize the properties of the photocatalysts and to demonstrate their efficiency in solar-to-hydrogen conversion. Main missions • Design the setup