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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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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 11 days ago
) Optimization and parameter identification methods Data-driven modeling and machine learning Physics-Informed Learning (or hybrid modeling approaches) Handling and analysis of large-scale datasets (e.g., mobility
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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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the impact of collective effects in high space-charge regimes. The Accelerators and Ion Sources Pole of LPSC is involved in the design, construction, and operation of the PERLE machine, particularly in
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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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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 7 days ago
children, moving home, etc.) Possibility of teleworking and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and
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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 | 7 days ago, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage 2 788 € Gross Monthly Salary Selection process Please send your
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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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