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submissions for continuing review, data use agreements, and protocol amendments. • Support development and testing of computational tools (e.g., algorithms for EMG signal analysis, data visualization
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
MED-CORE-CARD Full Time 88307BR Job Summary The Cardiovascular Genetics Center at University of California, San Francisco is seeking an experienced bioinfomatician to faciliate several lines
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simulation software. Develop algorithms and techniques that reinvent signal understanding and processing. Collaborate closely with the tight-knit members that make up the Simulation Team and collaborate
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Collaborate with students to translate research algorithms into working implementations. Lab Operations & Equipment Management (20%) Maintain and manage lab equipment inventory, including drones, sensors, 3D
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that cause diarrhea and pneumonia, intracellular signaling, pathogen adhesion, genetic determinants involved in susceptibility to infection, vaccine development, and identification of novel antimicrobial
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of themselves. The candidate will organize, manage, and curate big data on the cluster and find biological patterns in a wide range of genetic and epigenetic sequencing and imaging data to facilitate the paper
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, and digestive diseases, to genetics, genomics, neurosciences, and women's health. You can continue your career journey with us! The Slomka Laboratory focuses on developing innovative methods for fully
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, implementation, maintenance, and modification of complex research projects involving data collection, algorithms, data manipulation, analytical modeling, data warehousing, and computer applications and reporting
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cellular biology. His research integrates systems biology, mathematical modeling, bioinformatics, algorithm development, and the creation of open‑source software designed for broad usability. Benefits
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uncertainty/sensitivity analysis. Develop and apply predictive models, machine learning algorithms, and data‑driven analytical methods. Support the development of generalizable, scalable physical system models