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multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the interface of computational
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 hour ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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computers lack such abilities. The goal of the Adaptive Bayesian Intelligence Team is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with
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the support of and under the supervision of a Principal Investigator. To become familiar with the publication process. To acquire generic and transferable skills (including project management, business skills
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(https://www.hsph.harvard.edu/lin-lab/ ), Professor of Biostatistics and Professor of Statistics. The postdoctoral fellow will develop and apply statistical, machine learning (ML), and AI methods
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spearhead technical development, data analysis, and publication efforts for a student-led initiative using OpenBCI hardware, EEG recordings, eye-tracking integration, and machine learning to enhance neural
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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of surrogate optimization for supercritical fluid extraction / supercritical fluid chromatography (SFE/SFC). There will be the option to teach courses in the IMSE Department. Eligible candidates must have
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application of AI and machine learning models to interpret complex X-ray datasets, and the integration of experimental and computational insights to generate actionable knowledge that advances sustainable metal