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Field
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algorithms (such as machine learning or clustering). Familiarize with the multi-level data and how to model them in a polystore architecture [2] or similar (Month 1 – 12). Develop an environment to test the
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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networks are not well-suited to the computational constraints of FHE. The project aims to develop more efficient neural network architectures tailored for encrypted computations. The postdoctoral researcher
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atmospheric sciences • Knowledge of cloud or aerosol physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a
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, funded by the ANR P2S2 project. The position is available initially for a fixed-term duration of 2 years, with the possibility of extension for 1 further year. The P2S2 project aims at developing parton
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data from public or commercial databases and develop algorithms using existing libraries. Based on the previously identified resources, the Ph. D. thesis will then focus on the extraction of oxides and
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. Côte d’Azur & INRIA), will be focused on the development and the understanding of deep latent variables models for unsupervised learning with massive heterogenous data. Although deep learning methods and
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language models to whole genome sequencing data - Develop algorithms and neural network architectures for the prediction of structured outputs (i.e. trees, graphs) - Implement and develop methods
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electrical stresses. Specific goals include: - Development of a hybrid model combining degradation indicators and AI-based algorithms. - Integration of the model into an online monitoring framework
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