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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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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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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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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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Qualifications/knowledge : PhD in computer science, with a specialisation in computer vision, digital geometry processing and/or machine learning. No specific knowledge about plants is required. Operational skills
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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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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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tasks and zero-shot evaluation in linguistic analysis. Profile • Master’s degree (M2) or PhD in computer science, NLP, machine learning, deep learning, or a related field. • Strong experience in machine
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are looking for a motivated postdoc with solid experience in bioinformatics and in machine learning. The position centers on SRPs and LLPS, and the successful candidate will contribute to several ongoing
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processing, visualization. You will explore new avenues in coherent imaging, e.g. exploiting machine learning or introducing new techniques exploiting the EBS-enhanced coherent photon flux. You will also