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Field
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spatial transcriptomic data. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex
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research environment. Collaborate effectively with multidisciplinary teams across immunology, molecular parasitology, bioinformatics, systems biology, and clinical research. Present research findings
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products, are fundamental components of human nutrition. At the same time, the demand for high-quality gluten-free products has increased rapidly, creating an urgent need for ingredients that can effectively
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analysis, data organization and quality control, software testing, visualization of research data, scientific documentation, and general laboratory research support while working collaboratively with a
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recognized leader in cartographic design, dynamic mapping, mobile application development, spatial data analysis, visualization, and GIS. The Lab conducts interdisciplinary collaborative projects with research
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
work with Global Food Research Program (GFRP) Co-Directors, Professors Shu Wen Ng and Lindsey Smith Taille. GFRP research projects include evaluating and informing the effect of food and nutrition
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to understand dynamic molecular mechanisms underlying protein function. We integrate structural biology, electrophysiology, quantitative biophysics, and computational approaches to visualize transient molecular
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written, oral, visual, and interpersonal communication skills, including the ability to explain technical work clearly to both technical and nontechnical audiences. Demonstrated experience working directly
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reproduce clonally (the clones can be distinguished visually). The dynamics of the species and clones are very rapid, in terms of both density and the number of sites occupied. These species have recently
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part of change Development of novel image and video representations Research on alignment between different visual representations, e.g. foundation models Research on properties of learned visual