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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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-Language Models (VLMs). The postdoctoral researcher will explore how advanced generative and multimodal models can be designed, adapted, and applied to challenging vision tasks such as image understanding
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. Indeed, the methods currently used rely on optical image databases of various avalanche observations. A deep neural network was trained on this data to enable automatic avalanche detection FIGURE 1 (a) [1
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in the integration of microscopic imaging with transcriptomics, through the development of variant models for modality fusion. The postdoctoral position will be part-time within the computational
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neuromodulation therapies such as deep brain stimulation. The team combines intracranial recordings and EEG, brain imaging, brain stimulation, modelling and advanced signal analysis to link neural dynamics
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focused on deep-phenotyping of individuals with autism and controls including brain imaging (MRI, fMRI, DTI and EEG) and a battery of cognitive tests. Our group is currently developing new methods
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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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(particularly Deep Learning), will also make it possible to leverage the collected data to enrich knowledge of ovine behavior. The candidate will join a dynamic research group within the Image/Vision team
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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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and economy that respect people and their environment. We are looking for our future postdoctoral researcher in optimization for statistical learning to join the Image, Data, Signal (IDS) department