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Description We have an opening for a Postdoctoral Researcher to contribute to experimental heavy-ion physics, detector development, and scientific machine learning within Lawrence Livermore National
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important problems in biology and medicine. We seek outstanding researchers with training in AI/machine learning, computer science, statistics, mathematics, physics, or related quantitative fields
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integrates single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic
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rigorous evaluation methodology for interpretability claims, i.e., faithfulness, stability, and causal grounding. Research, design, implement, and apply advanced machine learning methods for multiple
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backgrounds in biomedical informatics, computer science, machine learning, statistics, data science, computational biology, or related quantitative disciplines. Experience with longitudinal health
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machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that
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technical expertise in machine learning, artificial intelligence, and data modeling approaches. We are committed to investigating the mechanisms of brain aging and developing innovative interventions to
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large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly refurbished primate facility, state‑of‑the‑art Neuropixels rigs, and high
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cardiac surgery outcomes research and predictive analytics using advanced statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data
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density in regulating these processes. The insights gained from these empirical studies will be paired with computational biology and machine learning to develop predictive AI models of how cells