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an advantage Strong background in machine learning for image analysis and computer vision, ideally involving microscopy, time-lapse imaging, or other high-dimensional scientific imaging modalities
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Post Doctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI
. The host research group leads a number of projects on the development of theoretical and applied methods in statistical modelling, medical imaging data analysis, cancer omics, precision medicine and
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bioinformatic analysis of the resulting datasets. You will work to integrate proteomic data with orthogonal single-cell modalities including transcriptomics and imaging, and collaborate closely with clinical and
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following neuroimaging methods and modalities: functional connectivity, representational similarity analysis, decoding techniques; structural connectivity, magnetic resonance spectroscopy; quantitative MRI
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disease. Another primary focus is the cross-modal integration of Alzheimerâ™s disease genomics with brain-imaging derived phenotypes to elucidate how genetics contributes to disease heterogeneity. Overall
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machine learning models. Working with extremely large, multi-modal datasets. Prior experience in analysis of clinical health records, and time series data are highly preferred. Qualifications Requirements
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disparities in Alzheimer’s disease. Another primary focus is the cross-modal integration of Alzheimer’s disease genomics with brain-imaging derived phenotypes to elucidate how genetics contributes to disease
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differentiation of pluripotent stem cells into retinal cells and tissues. The work will specifically employ scalable culture modalities and bioreactors. This work will be undertaken in close collaboration with
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studies. The position depends little on the volume of collected data: its validation relies on formal analysis, ablations and targeted studies. Where to apply Website https://emploi.cnrs.fr/Offres/CDD
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skills in multivariate statistics and the measurement of change Familiarity with one or more of the following neuroimaging methods and modalities: functional connectivity, representational similarity