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the competent authority of the Ministry of Higher Education, Research and Innovation (MESR). "Video content security in a deep learning coding architecture" Over the past few decades, numerous video compression
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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during surgery or endoscopic exploration. This postdoctoral position aims at developing innovative deep learning algorithms to help histology classification. Both classical histology based on hematoxylin
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research
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, deep learning, and cognitive psychology and ergonomics. The EnACA project consortium includes the Computer Science, Image, and Interaction Laboratory (L3I/EA2118, University La Rochelle), the Fundamental
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(pore sizes below 100 nm) structures. Excellent scintillation properties, particularly a high radioluminescence light yield. Maximal transparency to ensure that luminescence flashes can be detected deep