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16 Apr 2026 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique de Paris-Nord Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage Researcher (R1) Application Deadline 6 May 2026 - 23:59 (UTC) Country France Type of...
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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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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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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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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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