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, demonstrable experience in mass spectrometry-based proteomic analysis or in RNA sequencing, as well as a background in immunology and biochemistry; - Experience in quantitative proteomics data analysis and de
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cities and databases. Mandatory requirements: PhD in engineering, data science and computing, mathematics, or statistics; experience in engineering, data science and computing, mathematics, or statistics
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collaboration and provides access to high-resolution molecular analysis technologies. Mandatory requirements • Experience preparing clinical samples for multi-omics analyses, particularly transcriptomics and
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TEM/STEM experiments using nanometer-sized electron beams. Development of correlative protocols for ePDF and fluctuation microscopy (FM) data acquisition and analysis, focusing on middle-range order
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at: https://forms.gle/qxf5vo5kKmf9sBNV7 . Mandatory requirements: Bachelor’s degree in Biological Sciences and a PhD in Biotechnology, either completed or with the defense scheduled before the start of
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Recherche Scientifique (CNRS). The fellow will work on the project's “Decarbonization” pillar, with a focus on the analysis of the spatio-temporal dynamics of peri-urban forests, using LiDAR remote sensing
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: a continental data synthesis and meta-analysis” is available at CCARBON, a Research, Innovation and Dissemination Center (RIDC) supported by FAPESP and hosted by the University of São Paulo's Luiz de
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for general image classification tasks. We are particularly interested in applications involving weather radar imagery and medical imaging. Mandatory requirements: Ph.D. in Computer Science, Mathematics
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, microstructure, and properties. Data analysis and preparation of scientific papers, reports, and conference contributions. Mandatory requirements: PhD in Materials Engineering, Metallurgical Engineering, or
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implementation, surveys, quantitative analysis, qualitative data analysis software, team coordination, and fieldwork; publications in leading journals and experience presenting findings to public officials. How