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of researchers. The end of data taking being scheduled for January 2027, the student will make a significant contribution into data analysis toward the publication of the final results. The work will
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of the team are structured around 3 research aeras: • Research Area 1: On-line analytical methods via microfluidic devices for the quantification of air pollutants; • Research Area 2: Off-line analysis methods
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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STLO) (https://eng-stlo.rennes.hub.inrae.fr/ ). This work is part of the “Predicting the browning of dairy powders by kinetic modeling of Maillard reaction and caramelization during drying and storage
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-catenin pathway. The VASC-OPATH project proposes a translational strategy combining the analysis of paediatric samples with the functional validation of the identified molecular mechanisms in an innovative
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of deposited sequences. A larva–adult association component through sequencing will make it possible to assign a specific identity to morphologically unidentifiable larval stages, and an integrative taxonomy
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of crystallization conditions; preparation of samples for Cryo-EM and cryo-electron tomography; data collection and analysis; communication of results at conferences, scientific workshops, and thesis monitoring
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for Systems Analysis and Architecture (LAAS) within the MechaBioFluidics team, which has extensive expertise in microfluidics and microfabrication (a technology platform of the national ReNaTech network), in
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 30 days ago
the sensitivity and uniqueness of information used to express user intent, and estimating the consequences of data leakage, including re-identification risks. Sensitive data-flow analysis: tracking how personal and
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. Formalizing heritage reconstruction reasoning, including comparative analysis, chains of evidence, and source hierarchies, as computational models. 2. Developing controlled generative models capable