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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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characterization of nanocomposite films and interfaces. Interpret the experimental results. Highlight these results in relation to the work in the literature. Write scientific articles and communicate the results
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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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” 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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proposed for improving the accuracy of medical diagnoses, identifying biomarkers and understanding glioma features in MRI data. The successful candidate will work in particular on : - Development of a pre
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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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of performance - Ex-situ and In-situ optical properties of single layers and multi-layered devices - Devices Conception and building - Interactions with the partners of the EC-LIPSE project - Handling