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Field
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data sources in support of assigned research projects Designs reports, runs advanced queries, and develop and implement data transformation algorithms in existing database systems to support data
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of Department of Ophthalmology in the University of Colorado School of Medicine focuses broadly on quantitative and machine learning techniques in multiple modalities of medical imaging (e.g. fundoscopy images
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responsibilities will span all stages of research, including collecting data of in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical analyses, running
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Head and Neck cancer patients. This person will be working with our multi-disciplinary cancer specialty clinics and tumor boards. This individual will be expected to develop algorithms around national
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, developing, and implementing AI systems and data pipelines that enhance and automate university operations across multiple departments. This role is crucial in transforming manual processes into AI-driven
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scale and resolution. This ambitious project spans multiple institutes including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute, bringing
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multiple faculty clients at a time, and these engagements might last from a few days to several months. Your role will require you to listen carefully and critically to understand the faculty member’s
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architectures such as convolutional neural networks, transformers, and diffusion models. Proven experience building AI solutions using classical ML algorithms such as decision trees, gradient boosting machines
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maintainable software code according to software best practices. Designing software applications and algorithms to support RF network projects and applications. Deploying and supporting software delivered
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across disorders. Experience with multiple sequencing platforms including both short- and long-read sequencing is preferred. Bioinformatic approaches to identify targetable regions of the genome