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Field
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the world’s most challenging problems through artificial intelligence and data driven algorithms and systems. CSMD creates the mathematics, artificial intelligence, and architecture-aware algorithms
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the development of data processing scripts and pipelines to the development, distribution, and maintenance of free software for the analysis of genetic data. This is an opportunity to work in a challenging and
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following department/specialty algorithms, the Scheduler critically evaluates the nature and urgency of scheduling requests to provide comprehensive clinical ambulatory scheduling for providers within
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all aspects of the processing and analysis of genetic data, ranging from the development of data processing scripts and pipelines to the development, distribution, and maintenance of free software
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supercomputers, and computational imaging. Research Areas of Interest (include but are not limited to): Vision Transformers and foundation models for scientific and biomedical imaging Federated and distributed
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University of California, San Francisco | San Francisco, California | United States | about 5 hours ago
. This specialist assists in creating pipelines and configurations on a Linux-based distributed file system for Very Large health data, premise-hosted as well as public cloud based. Such data will be clinical
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, transmitted, and prevented, as well as how they are distributed throughout the population. Such information plays a critical role in guiding policies and other evidence-based strategies to promote health and
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in real-world settings; algorithmic accountability, bias/fairness, data justice; Indigenous data sovereignty; global and cross-cultural perspectives) AI Literacy, Pedagogy, and Instructional Design (AI
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and financial planning tools; this includes modeling new algorithms, creating functional specifications, and testing and/or validating new financial planning tools. Collaborate with other members
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; conversational systems; knowledge organization) Human-Centered, Participatory, and Ethical AI (co-design with communities; evaluation in real-world settings; algorithmic accountability, bias/fairness, data justice