371 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at CNRS
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-depth expertise in Computer Vision and Photogrammetry. - Mastery of state-of-the-art Neural Rendering (NeRF, NeuS, SDF). - Knowledge of Photometric Stereo methods. Operational Skills: - Advanced
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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to compute the predicted ZTs via first principles calculations. - Computer simulations: ML + DFT - Scripting (Python) - Analysis of the results + writing publications - The position is part of an ANR-DFG
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
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of Research ExperienceNone Additional Information Eligibility criteria - Computer encoding - Excellent written and oral communication skills - Experience in manuscript writing is desirable - Ability to work in
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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FieldComputer scienceYears of Research Experience1 - 4 Research FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria Skills/knowledge: computer vision, neural networks