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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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a family of specific peptide substrates for the PKs of interest (PKs of the JAK, GRK, IRAK, RIPK and ERK/MAPK families) (2) developing and optimizing fluorescent peptide biosensors from the identified
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of optimal plasma parameters (exposure time, power, gas composition) before their application to more complex cellular models. In a second stage, the PhD student will develop models of chronic infected wounds
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optimize a multimodal pretargeted radionuclide therapy approach for PDAC, leveraging the simultaneous targeting of both neoplastic and stromal targets to enhance therapeutic outcomes and reduce associated
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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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synthesis, purification and characterization Electrochemical and photophysical studies Solar cell design and optimization Bibliography Writing of reports and articles The CEISAM lab federates research
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cavitation and the effects observed on biofilms and bacteria, and to understand these links, in order, in the long term, to optimize and control this type of therapy. The profile of the PhD student (M/F) must