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Field
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develop cutting-edge algorithms and AI-based solutions for data processing and validation and provide scientific expertise for the implementation of future remote sensing missions. The team’s work bridges
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resources. Maintains awareness of subject and disciplinary repositories of potential interest to the OU research community. Maintains awareness of tools and algorithms for computationally centered, data
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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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machine learning algorithms. Re-train models as needed to address new challenges, or novel goals. Aid investigators and stakeholders in the validation of results. Generate detailed reports and presentations
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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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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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, 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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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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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
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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