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Field
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
of models and observational data using Python, Matlab, R, etc. Expertise required in developing new models, algorithms for dispersion, gas-phase chemistry and/or aerosol microphysics model Preferred
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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external) provides algorithmic and data-driven solutions providing fairer access to public educational resources. The lab enables school districts to optimize large-scale equitable resource allocation
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unravel the complex relationships between land use changes and fire regimes over the past 60 years. The successful candidate will lead efforts to: Develop advanced deep learning algorithms for classifying
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. Combining operators You can combine the operators above to fine-tune your results: +"artificial intelligence" algorithm +robot -machine → Shows results only if they include artificial intelligence (as an
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
, algorithms for dispersion, gas-phase chemistry and/or aerosol microphysics model. Preferred Qualifications, Competencies, and Experience Desired qualifications are a) atmospheric and aerosol chemistry, b
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applying interpretable AI / machine learning / deep learning / information-theoretic methods and algorithms in the context of multiscale biological networks, ranging from molecules (protein chemistry) to
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single-cell sequencing, spatial transcriptomics, and machine learning algorithms to to understand, at the tissue and organ level, how specific cellular communications—from synaptic connectivity to neural
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees