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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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” 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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to efficiently identify materials with optimal mechanical properties and controlled degradability. The primary task is to develop techniques for synthesizing degradable polymers and copolymers using ring-opening
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research contractors. Through the design and characterization of new materials, the laboratory's approach leads to the optimization of a wide range of properties for targeted applications. The position is in
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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
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-processing pipeline for high-field MRI medical data (normalization, denoising, spatial registration) to optimize the quality and consistency of data used in analyses, and to facilitate the search
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Process: designing, preparing, shaping and characterizing materials in order to discover, control and optimize specific functions. The ICMCB carries out fundamental research on model materials and/or