11 algorithm-sensor-"CSIRO" Postdoctoral positions at Stanford University in United States
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gyroscope. Required Qualifications: Doctoral degree issued prior to appointment start date Strong background in photonics, especially lasers and sensors Required Application Materials: CV Stanford is an equal
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be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single-cell RNA-seq, ADT-seq, ATAC-seq, DNA
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-cell and spatial-omics research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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together experts in systems neuroscience, AI, and engineering. This ambitious initiative promises to offer unprecedented insights into the brain's algorithms of perception and cognition while serving as a
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signals or kinematic signals from wearable sensors in a demand-based fashion. Additional projects include the development of a remote monitoring tool for Parkinson’s disease and other disorders which
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highly motivated and collaborative Postdoctoral Fellow with an interest in optics, device physics, and/or environmental sensor integration for ocean observation. We are an interdisciplinary group
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; familiarity with cloud computing tools and big data workflows A track record of completed research projects Well-written, peer-reviewed papers are expected Interest in using EHR metadata, wearable sensors, and
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will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user