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postdoctoral research project. The project focuses on the development and application of a remote sensing algorithm for monitoring the physicochemical properties of aerosols based on optical measurements taken
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to the construction of the detector at the LNGS. Data taking should start in 2029. The subject of this postdoctoral position, funded by the CNRS for two years, is to prepare the analysis of the first data in order to
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the development of efficient algorithms and codes for multilinear algebra, with a particular focus on the use of innovative parallel programming models and tools. In the context of this task and as part of the Exa
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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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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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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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Polytechnique de Paris, is one of France's top 5 general engineering schools. The mainspring of Télécom Paris is to train, imagine and undertake to design digital models, technologies and solutions for a society
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further year. The P2S2 project aims at developing parton-shower algorithms with unprecedented (logarithmic) accuracy for jet substructure at the LHC. The project also has connections with analytic
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9 Feb 2026 Job Information Organisation/Company CNRS Department Sciences et Ingénierie, Matériaux, Procédés Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 3 months ago
accurately study the selected algorithms, participate in the development and maintenance of the PEPit (https://pepit.readthedocs.io/ ) and AutoLyap (https://github.com/AutoLyap/AutoLyap/ ) software packages