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dimensional information, classification and/or deep learning methods may also be developed. In addition, the complementarity between the different data sources used (particularly between aerial LiDAR data and
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optimizing a superstructure automatically generated using a deep learning approach. The project brings together the LRGP (Reaction and Engineering Laboratory, CNRS-Université de Lorraine), the LPSM
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closely with our collaborators to establish a deep learning-based image analysis pipeline. The successful applicant should hold a PhD in cell biology or neuroscience. Previous experience in live cell
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-simulation databases opening up the possibility of interpretations by deep learning techniques. A numerical database is provided by the team at CORIA, which is also in charge of developing the optical models
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Additional Information Eligibility criteria Transversal knowledge required : - Expertise in machine learning and deep learning in particular - Knowledge in ecology, marine biology, or oceanography would be a
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of the mines must also be considered. Recent advances in the geotechnical and geomechanical fields have led to a significant increase in the usage of machine learning (ML), thanks to its computational power and
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and