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the integration of large-scale biological datasets derived from both the host and the microbiome, employing advanced statistical methods and cutting-edge artificial intelligence techniques to uncover novel insights
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SAXS and Large scale facilities SAXS (from proposal writing to execution of experiments and data analysis for the latter) Experience with protein purification Advanced knowledge in research design and
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data mining. The group provides a strong network to local AI expertise (e. g. Hessian.AI, TU Darmstadt), large scale compute infrastructure, as well as a broad international network (Stanford, UC San
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 1 month ago
samples such as serum/plasma, biopsies and whole blood for analysis and large databases, which include clinical information on patients. The group’s homepage: https://www.skane.se/en/about-us/research/for
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collaborative research on immune development and responses to respiratory viruses. Analyze clinical and large-scale datasets (e.g., genomic, transcriptomic, proteomic data). Design and execute experiments using
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Large-scale and in-depth characterization of optimized Channelrhodopsin variants for basic research in neuroscience and future optogenetic therapies Automated (Syncropatch384, Nanion) and manual patch
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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supervise cutting-edge interdisciplinary research building on the analysis of large-scale blockchain data. This will include, but is not limited to: Designing, implementing, and maintaining scalable pipelines
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RNA-seq data for testing and validation. Perform data analysis on large-scale RNA-seq data in pediatric cancer. This may involve the analysis of both scRNA-seq, bulk RNA-seq as well as new single-cell
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causal networks that govern cellular behavior from large-scale single-cell datasets. Our group has pioneered computational approaches for: Inferring causal networks from Perturb-seq (interventional single