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Field
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to the computational analysis of visual culture in the humanities. The postdoctoral researcher will work closely with the project PIs, Lauren Tilton and Taylor Arnold, to develop and implement educational resources
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), combining state-of-the-art in vivo approaches with quantitative data analysis. About the lab and environment The Rebola Lab (https://therebolalab.org ) studies how variability in synaptic function across
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projects integrating omics, clinical and medical imaging data, contributing to biomarker identification and the development of computational approaches for healthcare. Key responsibilities include analysis
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, processing, visualization, interpretation…) Analyze single-cell and spatial transcriptomics datasets Decipher metabolomic patterns Perform high dimensional data analysis and integrative ‘omic’ data analysis
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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visual dimensions in the context of face, animacy, and object representations more generally. We collect and analyse human behavioural and brain imaging (fMRI and M/EEG) data. We also analyse macaque
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and collaboration in low-and-middle-income countries and good knowledge of R or other software for data management, analysis and visualization. The position does not require teaching, but it may be
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. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management
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; Skills: brain surgery and behavioral tests on rodents, or advanced image and data analysis; Experience in iDISCO brain clearing, light sheet microscopy and data analysis, miniendoscopes application in a
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given to candidates with expertise and experience with large language models (programming, benchmarking, human-centered evaluation), strong data science / computational modeling / statistical analysis