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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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Posting Details Posting Details Posting Number 0801675 Classification Title Postdoctoral Research Associate- Autonomous Systems and Machine Learning Working Title Department College
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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The Machine Learning (ML) group led by Professor Ricardo Henao at KAUST (King Abdullah University of Science and Technology) has multiple openings for Postdoctoral level positions to conduct
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are looking for young scientists who are passionate about water desalination and machine learning with its potentially transformative applications in research and industry. The two-year post-doctoral position
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19.07.2022, Wissenschaftliches Personal The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current
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Postdoctoral Associate Position on Blockchain and Machine Learning in the Dr. Kuo Lab at Biomedical Informatics and Data Science, Yale University New Haven, Connecticut (USA) About the postdoctoral
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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures