21 network-coding-"Chung-Ang-University"-"Chung-Ang-University" Postdoctoral positions at Nature Careers in United States
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master's degree (or equivalent) and PhD in Mathematics/Statistics/Data Science Research expertise in the analysis of complex systems Familiar with network analysis, concepts of resilience (e.g. adaptive
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. amino acid and lipid metabolism), signaling pathways (e.g. mTOR and nutrient signaling), and systems-level regulatory networks that regulate basic T cell and dendritic cell biology and antitumor immunity
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, biomedical engineering, or a related field. • Experience with coding software such as Python, R, or Matlab. • Familiarity with collaborative coding software such as GitHub and database management
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. Description We are looking for a postdoctoral computational biologist to decipher the genomic regulatory code controlling gene expression in plants. Within the ERC-funded multiCODE project, we aim to unravel
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. Description We are looking for a postdoctoral molecular biologist to decipher the regulatory code controlling gene expression in plants through the engineering and large-scale validation of synthetic promoters
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The Özel Lab at the Stowers Institute for Medical Research has openings for postdoctoral researchers to pursue independent projects investigating the gene regulatory networks that control neuronal
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networks, and study data from both mice and human patients to better understand normal cognition as well as the pathomechanisms of neurodegenerative dementias, including Alzheimer’s and Parkinson’s disease
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, pharmacogenomics, statistical genetics, or population genetics and experience in statistical and computational analyses of high-throughput omics data Ability to code in one or more scientific programming languages
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. amino acid and lipid metabolism), signaling pathways (e.g. mTOR and nutrient signaling), and systems-level regulatory networks that regulate basic T cell and dendritic cell biology and antitumor immunity
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. Position Responsibilities: Develop and implement computational and systems biology approaches for network and hidden driver analysis from spatial omics and single-cell omics data. Collaborate with