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integrated modeling and machine learning for energy and circular-economy applications. The Post Doctoral Associate will design and execute simulation and machine-learning studies, analyze results
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in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 7 hours ago
learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental biology, chemistry, and medicine. Machine Learning and Life Science
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can be found on the PI’s website: https://aobol.github.io/AoboLi/ The position builds on the group’s recent development of SIDERIUS, an agentic framework for long-horizon machine-learning development
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details on our research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu/ . For questions, please email [email protected] Applications will
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( https://www.hsph.harvard.edu/lin-lab/ ), Professor of Biostatistics and Professor of Statistics. The postdoctoral fellow will develop and apply statistical, machine learning (ML), and AI methods
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( https://www.hsph.harvard.edu/lin-lab/ ), Professor of Biostatistics and Professor of Statistics. The postdoctoral fellow will develop and apply statistical, machine learning (ML), and AI methods
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models, causal inference and machine learning. Dr. Liu's research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a
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Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research sits at the
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brain disorders such autism spectrum disorder and bipolar disorder, by utilizing recent advances in genetics and genomics, and through collaborations with groups using machine learning. We are