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University of California, Los Angeles | Los Angeles, California | United States | about 20 hours ago
seeking applicants for a full-time postdoctoral scholar position in Computational Immunology and Genomics. The successful candidate will develop and apply computational, statistical, and machine-learning
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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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. The QML-CVC team (https://qml.cvc.uab.es/ ) is part of the Computer Vision Center at the Universitat Autònoma de Barcelona; the CVC is a center with over 150 persons working machine learning and computer
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. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You will experience
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Data Analytics, including but not limited to: “Data Analytics for Social Research”, “Applied Analytical Statistics for Social Scientists”, “Machine Learning with Social Data” and “Social Networks
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 12 days ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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actions, anticipate future behavior, and reason under uncertainty. The methodological scope includes machine learning, computer vision, multimodal perception, probabilistic modeling, and interpretable