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of this position will be development of custom neural networks for functional annotation of protein sequences. This is an Extended Temporary Employment (ETE) position. Outstanding UA benefits include health, dental
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
, Artificial Intelligence, Neural Networks, Computational Biology, Bioinformatics, Biomedical Informatics or a related field. Programming experience in a language such as Python or R. Experience in writing grant
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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mechanical engineering, or related field to apply. A strong publication record is encouraged and previous experience in areas such as Neural network vulnerabilities and defenses, Anomaly detection, Adversarial
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 29 days ago
increasingly utilizes big data, satellite imagery, register data, and advanced methods such as deep learning and neural networks to address major societal challenges related to spatial inequalities and
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execute a novel research program at the interface of sensory-motor systems, quantitative behavior, and neural computation. The Audette Lab is a new research group funded by the University of Connecticut and
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with Dr. Emily Becker and the NOAA Climate Prediction Center. Key responsibilities Train neural networks and quantify uncertainty to evaluate predictability Perform explainable AI (XAI) related research
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of research include quantum Monte Carlo methods, density matrix renormalization group and tensor network states, and artificial intelligence and neural networks, with particular focus on applying
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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and