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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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and mRNA translation. The successful candidate will establish live imaging tools to analyse translation dynamics in regenerating axons, using an ex vivo culture model previously optimized (Schaeffer et
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under periodic conditions. SOC and excited-state geometry optimizations will help identify ISC/RISC probabilities and the specific components involved. These insights will clarify how each molecular
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human cells and tissues. 1. Perform research, from experimental design and implementation to the writing of results: • Analyse the scientific literature for the project. • Design and optimize DNA
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. This is only possible thanks to the involvement of the Univ-Lille partner, which is developing these highly optimized data structures and partners (Paris-Saclay Univ) with a solid expertise in exploiting
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(cholesterol esters) and proteins; in particular, the person recruited will be responsible for optimizing the parameters for neutral lipids. • Develop and implement new methodologies for analyzing molecular
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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
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, and optimize sensor/microsensor responses. This interdisciplinary approach is essential to understand the changes of thermal/radiative properties by correlating them with the evolution of chemical and
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offering a postdoctoral position in multi-messenger astrophysics related to the KM3NeT, SVOM, and COLIBRI detectors. The main objective is to complete the development and optimization of the KM3NeT neutrino
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optimizations are still needed to adapt the translation of these mRNAs to the cell types of interest. As part of a collaboration with Chantal Pichon's team (University of Orleans), this project aims to use