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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
. Description: Uncertainty quantification is a field with wide applicability to the design, development, and science return of space science missions. There is growing interest for the tools and practices
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sets and developing algorithms. You should be highly motivated, self-driven, and possess strong work ethics, team spirit, and excellent collaboration skills. You will be responsible for the collection
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interdisciplinary and international team of computational and experimental scientists, gaining unique opportunities to develop novel computational tools and pipelines for the analysis and integration of genomic
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that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute
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components: - operational modal analysis to extract the modes of the probed medium, - algorithmic and experimental developments on the MSE method - and algorithmic and experimental developments on the MFP
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) Social Media Analysis (iii) Algorithmic Privacy, (iv) Analysis of Academic Collaborations, (v) Human-Bot interaction, (vi) Network Science. The ideal candidate is self-motivated and hard-working with a PhD
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methods to improve the deployment, adaptation capabilities and safety of robots and critical infrastructures. The developed algorithms will be evaluated on legged robots, wheel-based robots and under
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applications in the media change journalism, the public sphere, civic engagement and economic competition. We will develop and test new AI-applications to help solve problems such as disinformation, algorithmic
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project is to develop a high-performance computing framework for mass spectrometry proteomics to enhance efficient processing and interpretation of large datasets using deep learning algorithms and GPU
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developing or applying parallel algorithms and scalable workflows for HPC resources. Experience developing or applying privacy-enhancing technologies such as federated learning, differential privacy, and