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Lawrence Berkeley National Laboratory is hiring a Postdoctoral Researcher – Machine Learning for Materials Science within the Molecular Foundry division. Molecular Foundry is a Department of Energy
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using wearable electroencephalogram (EEG) during sleep. Our goal is to pioneer developments in wearable technology that integrate reinforcement learning to discover novel strategies for human interaction
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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-driven approach for optimizing the growth of semiconductor materials by combining machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap
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We work on highly topical, socially relevant issues and offer you the opportunity to actively shape change! You can expect a wide range of opportunities: Meaningful tasks: The opportunity to conduct exciting research in an international and multidisciplinary environment with outstanding...
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! Be
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solutions to compelling problems in energy and security. The Learning Systems Group at Oak Ridge National Laboratory (ORNL) seeks a postdoctoral researcher specializing in federated learning and privacy
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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seeking a highly motivated Postdoctoral Researcher to join the Machine Learning cluster. The position is part of a research project investigating how visual foundation models can efficiently acquire new