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FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria - Thesis in natural language processing with machine learning, - mastery of NLP and machine learning methods and tools
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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Ecole Polytechnique, in Palaiseau, France, and will consist of theoretical and numerical modellng. The thesis will consist of modeling turbulence using Machine Learning methods, in particular Physics
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feature filtering procedure to deal with the large feature set necessary to predict the thermoelectric ZT of a material. - Improve the already existing experimental dataset. - Apply different machine
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the creation of high-precision digital twins. Activity 1: Integration of Photometric Stereo in Meshroom - Implement processing nodes for normal field and intrinsic color estimation. - Integrate deep learning
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Eligibility criteria Instrumental optics and imaging (microscopy, camera detection) for biology. Skills in coding and experiment control. Basics of machine learning and/or signal processing. Teamwork
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the following ones. Exploration of active auditing techniques for large machine learning models, use of reinforcement learning, potential application to recommender systems. The PhD will mainly investigate
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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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use, utilizing innovative binary file analysis and deep learning to improve the security of computer systems. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5104-MYRLAU-003
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at the interface of biological physics, agent-based simulations and machine learning to turn quantitative imaging data into a mechanistic, testable model of spindle positioning. In particular, we expect