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Field
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Contribute your computer vision
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calculation of NMR parameters. - Experience in experimental NMR. - Basic knowledge of electrochemistry, magnetic and electronic properties, and solid-state chemistry. - Proficiency in the necessary computer
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with physics-based models Developing robust and adaptive methods for real-time parameter and state estimation Implementing machine learning approaches that preserve physical constraints while handling
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 2 months ago
(whether P-wave, S-wave, or combined datasets) in relation to the physical parameters being reconstructed (e.g., Lamé parameters, density, anisotropic properties). The goal is to design an inversion
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territories. The pedigrees reconstructed in each populations are sufficient to estimate some simple quantitative genetic parameters, but they are incomplete and contain errors, which greatly limits
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for nutrients, light, temperature, the influence of zooplankton or more generally higher trophic levels, as well as other parameters such as parasitism and allelopathy, if possible. The model can build on
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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parameter space, and using and/or developing agent-based models for the movement and behavior of fish in rivers. Presenting material at conferences, writing research papers for publication, and/or assisting
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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1: Extension of the coverage and performance of the HLA-Epicheck model through the addition of new antigens. This also includes optimizing the values of certain model parameters. Task 2: Evaluation