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Website: https://med.stanford.edu/stankovic-lab.html (link is external) https://hellerlab-stanford.net/ (link is external) How to Submit Application Materials: Please send the following materials in a
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based on an individual-centered spatial analysis approach (geomatics) using AgentBased Modeling to model mobility and its evolution. The postdoctoral researcher will intervene in the second phase
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The Department of Genetics, Cell Biology, and Development comprises faculty from both the Medical School and the College of Biological Sciences. Faculty members are engaged in research in three key disciplines
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, macOS) comprehensive knowledge in the field of cybersecurity expert knowledge of reverse engineering mobile devices/smartphones documented experience in developing mobile applications for iOS fluency in
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 2 hours ago
Position Information General Information Position Number 009668 Vacancy Open to All Candidates Working Title Facility Planner - Mobility Position Designation EHRA Non-Faculty Employment Type
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-dependent MLCS regulation on lysosomal trafficking, mitochondrial quality control and hypoxia-induced cell death. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd
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Single-cell high-throughput sequencing technologies generate unprecedented volumes of molecular data at cellular resolution, opening new avenues for the application of machine learning
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or models in close collaboration with experimentalists. This project focuses on deciphering tumor cell plasticity, including method evaluation/development and biological interpretation of the results. In
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pathways that regulate cardiac organ development and homeostasis. Our lab performs computational and wet-lab work, though the ideal candidate should have experience in molecular biology, genomics, cell
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA