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and fed with data available in the literature. The model will first be developed online and then reduced and optimized for integration into embedded electronics, taking into account performance
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on the principles and implementation of biological experimentation techniques -Establish and optimize data collection and control procedures -Perform data processing -Organize data formatting and storage -Participate
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to optimize the existing experimental setup, develop new methodologies to conduct the proposed experiments, analyze and interpret experimental data in collaboration with fellow researchers in and outside
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rechargeable batteries. In this context, one of the challenges involves optimizing the electrolyte, which determines the stability of the metallic anode, the electrochemical window, and interfacial processes
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. The project will involve: • Synthesis and characterization of proton-conducting ceramic materials and composite electrodes. • Development and optimization of acid-base infiltration protocols for surface
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of such high-performance fiber laser platforms requires advances in the development of high-reliability fiber components and optimized doped fibers. The first objective of this PhD project is to explore
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leveraged to improve existing modulation models describing how large scales alter heat transfer. Optimal oscillations will be designed using reinforcement learning. Extending inner-outer interaction models
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- Solid knowledge of existing literature in optimization and/or symbolic computation - Strong skills in programming with scientific and/or symbolic computing tools Website for additional job details https
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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the context of LOFAR 2.0 (Harish Vedantham et al., ASTRON, Netherlands), MeerKAT, and SKA (Oleg Smirnov et al., SARAO, South Africa). It is funded by LIRA, which also provides optimal support to its doctoral