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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 10 days ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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generation initiative. Our laboratory has expertise in deep learning, including deep reinforcement learning, large language models, and the theory of deep learning. The candidate will develop DRL algorithms
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bioinformatics analysis pipelines for processing RNA-seq, single-cell RNA-seq, genomics and proteomics data. Develop novel algorithms and integrated data visualization applications when existing software packages
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should be implemented to speed-up the acquisition rate and to optimize the setup sensibility and efficiency. Then implementation of new algorithms to build new biomarker maps should be also developed
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developed: Collaborate in studies of a combinatorial optimization problem of current interest. Propose new algorithmic solutions for its resolution. Cooperate in the extensive experimental analysis
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developed: Collaborate in studies of a combinatorial optimization problem of current interest. Propose new algorithmic solutions for its resolution. Cooperate in the extensive experimental analysis
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between these sub-areas. Essential Duties and Responsibilities: We are looking for a Postdoctoral Research Associate who is interested in the development of quantum algorithms and related quantum informatic
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description: Work Area: Research and development of new algorithms for processing and classifying physiological signals in ambulatory systems Project overview: Processing of physiological and inertial signals
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research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in