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general guidance. Excellent organizational skills and demonstrated ability to accurately complete detailed work. Physical Requirements*: Frequently stand, walk, twist, bend, stoop, squat, grasp lightly, use
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of illness, reactions to medications, general behavior, and/or general physical condition. Assesses signs, symptoms, reactions, behaviors, or general appearance to determine normal versus abnormal
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The Department of Physics and Astronomy at the University of California, Riverside (UCR) seeks applications for a postdoctoral position for research in experimental/observational cosmology
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tomography data. Develop new data-driven methods that leverage physics-informed machine learning for reconstructing non-rigid deformations and generative modeling of conformational heterogeneity in electron
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Stanford University, Department of Earth System Science | Palo Alto, California | United States | 9 days ago
ecological dynamics If you have expertise in machine learning (especially but not necessarily hybrid physical-machine learning models), wildfire, large-scale plant hydraulics, remote sensing of vegetation
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to maximize the future value of efforts. This will be an opportunity to learn how to create production quality systems. Basic Qualifications Currently pursuing a bachelor's degree in physics, Electrical
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, Chemistry, Physics, Pharmacy, or a related discipline with expertise in computation, molecular dynamics simulation, and statistical mechanics. For a further description of current research projects see https
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Opportunity employer. Cedars-Sinai does not unlawfully discriminate on the basis of the race, religion, color, national origin, citizenship, ancestry, physical or mental disability, legally protected medical
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systems with the capacity to learn ad hoc construals of physical scenes that depend on current goals. The second component of this project entails developing AI systems that are capable of coordinating with
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collected from different sources (e.g., self-report surveys, GPS, phone metadata). The postdoc will use computational methods to process and interpret multi-modal behavioral and contextual data collected from