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The Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is seeking a Postdoctoral Researcher – Scientific Machine Learning & Computational
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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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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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brain MRI analysis, statistical analyses, as well as data acquisition and machine learning. Experience with stroke and federated learning is a plus. Job ID REQ20177725Posted Date 08/05/2026 Current
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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seeking applicants for a full-time postdoctoral scholar position in Computational Immunology and Genomics. The successful candidate will develop and apply computational, statistical, and machine-learning
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. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master various computer
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control and collaborative software development practices. Experience with tight-binding, density functional theory, and many-body condensed matter theory and associated software. Experience in machine
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 month ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time
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of Energy (DOE) experimental facilities. This role involves research and development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high