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, behavioural analysis, investigations of ALS human post-mortem tissue, spatial transcriptomics, and gene therapy. The successful applicant will have completed a PhD in Neuroscience with expertise in molecular
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technologies (e.g., spatial transcriptomics, CODEX, Akoya PhenoCycler). Proficiency in single-cell RNA sequencing, high-dimensional data analysis, bioinformatics tools, and programming languages (R, Python
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
statistics and the ability to apply quantitative analysis to ecological data A strong background in programming (preferably in R), including data manipulation, statistical analysis, and spatial modelling and
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emissions due to the large temporal and spatial variations in CH4 emission and its underlying processes. The lack of continuous observations of CH4 turnover hinders our understanding of CH4 emission
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in physical layer algorithm design and performance analysis Proven track record of publications in relevant IEEE journals and conferences In addition to the above qualifications, it is highly desirable
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behavior. Preferred Qualifications: Experience using quantitative spatial techniques to characterize neuron distribution. Brief Description of Duties: The Postdoctoral Associate will assist the Principal
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
scale analysis of multiplex spatial phenotyping across the tumor microenvironment with the possibility of integration of spatial transcriptomics. At the bench, the postdoc will perform confocal imaging
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of the Mississippi embayment), surface water hydrology and modeling, model coupling, and spatial/temporal data analysis. The successful candidate will work with Dr. Brian Waldron and CAESER faculty/staff on projects
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. Required Qualifications: A doctoral degree (PhD, MD, or equivalent) conferred by the start date. Proficiency in R/Python Experience with scRNAseq, and/or spatial proteomic/transcriptomic data analysis Growth
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial