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Field
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) About the Project Deep learning models, and in particular large language models (LLMs), have demonstrated remarkable capabilities but remain limited by their heavy computational requirements, lack
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the concerted chemistry of transition metals and protein-based radicals [1]. Our bioinspired artificial catalysts are being conceived and characterized as in vitro tunable model systems for understanding better
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proposes to combine in situ and remote sensing data, radiative transfer models, and chemical and physical analytical methods to better understand the fundamental properties and the impact of mineral dust
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reactor techniques will be used. The experimental database will serve to the validation of detailed and reduced chemical kinetic models and LES tools performed by the project partners. The research includes
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thermodynamics - Modeling of experiments and calculation of theoretical maximum efficiencies - Writing of reports and scientific articles Secondary tasks: - Presentations at international scientific conferences
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contribute to various aspects of the project, such as: - developing new theoretical approaches to model electrode/electrolyte interfaces - performing molecular simulations, such as molecular dynamics
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evaluation of algorithms for: perception in robotics; sensor based control and navigation ; interactive mobile manipulation; multi-sensor data modelling and fusion. This job offer takes place within
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/Qualifications • PhD in microbiology, molecular biology, or related field • Strong experience in bacteriology and molecular mechanisms of pathogenesis • Previous experience with insect models and host
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Inria, the French national research institute for the digital sciences | Bures sur Yvette, le de France | France | 3 months ago
physiological and neural recordings (EEG, fNIRS, ECG, blood pressure, and PPG) with advanced signal processing, mathematical modeling, and artificial intelligence. This project aims to provide new insights
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modelling, naturalist data usage. LanguagesENGLISHLevelExcellent LanguagesFRENCHLevelExcellent Research FieldEconomics » Applied economicsYears of Research Experience1 - 4 Additional Information Eligibility