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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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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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controlled power electronics to optimize the overall efficiency of electromechanical conversion. Ultimately, the system will be integrated into two thermal energy conversion cycles: a thermochemical cycle and
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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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critical for optimizing performance and is an important ingredient in the design of high performance parts in aeronautics, energy, transport, and plenty of other domains. The theory behind the aforementioned
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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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value-added materials. Objectives of the PhD The PhD candidate will focus on designing and optimizing green spinning processes for lignin-based fibers, relying exclusively on enzymes and water, avoiding
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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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complexity by guaranteeing solutions close to the theoretical optimum through local optimization methods. Third, the integration of user preferences allows generating personalized explanations according