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of agentic AI systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine physics-based modeling, machine
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approved drugs that target key dysregulated pathways/proteins in virally infected cells. The candidate will also assist with the development of liver and/or brain organoid cultures including iPSC-based
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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scanning (TLS), throughout hardwood-dominated locations in Virginia, including several Department of War installations and bases and large sections of the George Washington-Jefferson National Forest (2
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Carilion (VTC). About the Lab: The lab integrates state-of-the-art neural recording technologies, complex cognitive tasks, and computational models to investigate the neural basis of flexible cognition. From
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models, analyzing and interpreting experimental data, preparing manuscripts and grant applications, and mentoring trainees within the laboratory. A major component of this position will involve mouse
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. The candidate will apply a suite of statistical and physical models for integrating observations of different accuracies, improving predictions of future hazards. The work will further include research, writing
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or Immunology. The successful candidate will work on an interdisciplinary research project focused on murine models of brain injury and innate immune regulation. Qualified candidates will be expected to work in a
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or Immunology. The successful candidate will work on an interdisciplinary research project focused on murine models of brain injury and innate immune regulation. Qualified candidates will be expected to work in a
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or Immunology. The successful candidate will work on an interdisciplinary research project focused on murine models of brain injury and innate immune regulation. Qualified candidates will be expected to work in a