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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 5 hours ago
such as electrical power. Recent advances in machine learning present new opportunities to enhance the level of fault management and control in NASA's future power system applications. This work aims to
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), research is carried out in computer vision, robotics and machine learning. We are now looking for two postdocs in robotics and machine learning and computer vision. The successful candidates is expected
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feature filtering procedure to deal with the large feature set necessary to predict the thermoelectric ZT of a material. - Improve the already existing experimental dataset. - Apply different machine
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Graph Machine Learning and Graph Data Management At Section
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Are you inspired by the quest for new materials for solar cells, spintronics, and quantum technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you
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/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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Postdocs in Generative Machine Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures