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Design and conduct analyses linking vocal biomarkers to validated psychological scales (e.g., PAID, PHQ-9, GAD-7). Apply natural language processing (NLP) and AI techniques to large-scale audio datasets
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rodent studies—obtained by electrophysiology, fiber photometry, and calcium imaging in vivo recordings—the project will build a biologically grounded SNN model of memory de-association. The ultimate goal
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computer science or a related field. General: Knowledge of hospital environments and the healthcare sector, as well as innovative technologies: AI algorithms, image and signal processing, segmentation, modeling
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Application procedure: Interested candidates should send an e-mail with their CV, their transcripts and two possible referee contacts to marco.corneli@univ-cotedazur.fr and emmanuelle.vila@mom.fr
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Keywords Optogenetics, 2-photon activation, large scale electrophysiological recordings, mouse model Lab description Processing of auditory information in the brain is complex because information
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: Programming in Python and/or R Data science (e.g., tidyverse, pandas) Machine learning (e.g., scikit-learn) Deep learning (e.g., PyTorch, Keras3) (Optional) bio-signal processing and brain imaging (e.g., EEG
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number of techniques routinely used in the lab including histology, confocal microscopy and image processing. Prior expertise in cell culture, image analysis and coding would be highly appreciated. Part of
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imaging, omics, and structural biology platforms. The institute is affiliated with the University of Strasbourg and provides strong institutional support for securing national and European funding, as
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, Impresso - Media Monitoring of the Past (https://impresso-project.ch/ ) is an interdisciplinary research project that uses machine learning to pursue a paradigm shift in the processing, semantic enrichment
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R