339 machine-learning "https:" "https:" "https:" "BCBL Basque Center on Cognition, Brain and Language" Postdoctoral research jobs in France
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Post-Doctoral Research Visit F/M Postdoctoral position in web security, privacy, and online tracking
Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 4 days agoto apply Website https://jobs.inria.fr/public/classic/en/offres/2026-09962 Requirements Skills/Qualifications Qualifications - PhD in Computer Science (Security, Privacy, or Web Systems). - Good track record
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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 4 days ago
, tests should adapt to observations as they are collected. Main references: Information Lower Bounds for Robust Mean Estimation (Degenne and Mathieu, 2025): https://arxiv.org/abs/2403.01892 The numeraire e
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or machine learning applied to brain signals would be an advantage. We are seeking a highly motivated, rigorous and inquisitive researcher, ready to commit to a project at the interface between basic and
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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instruments; a computer and scientific data processing software; access to CNRS databases and documentary resources. Key stakeholders: The UTINAM teams (researchers, postdoctoral fellows, engineers, and
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. In particular, he/she will be expected to :• Select and evaluate the most suitable approaches from the wide range of machine learning and computer vision methods available in the literature, with
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of plant roots (https://www1.montpellier.inra.fr/wp-inra/ipsim/en/ ). Growing facilities and equipment for root hydraulic phenotyping are available in the laboratory. Where to apply Website https