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Responsibilities: 1) Model Development: Construct mechanistic coupled xylem–phloem models using first-principles representations of axial and radial transport including a Hagen–Poiseuille flow, Sampson flow
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adaptation, dataset shift robustness) Causal inference for risk prediction and decision support Fairness and trustworthy AI in healthcare applications Multi-modal representation learning and integration
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Postdoctoral Associate Early Childhood Cognition Lab at Duke / Dr. Tamar Kushnir Department of Psychology and Neuroscience Overview The Early Childhood Cognition Lab at Duke’s (http://www.ecclabduke.com
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community. EDUCATION/TRAINING: PhD in electrical engineering, physics, quantum theory/science, expertise in detection and estimation theory. PREFERRED: Statistical signal processing with knowledge of quantum
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interacting with a team of researchers from several universities. Knowledge of advanced statistics and experience working with MPlus, SPSS, R, and other statistical packages is required. Experience using large
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physics journal. Skills Good working knowledge of multiple programming languages for numerical computation, broad understanding of quantum theory, good scientific communication abilities. The intent
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partnerships with local Duke EM partners. •Strong quantitative skills in the areas of biostatistics, bioinformatics, and/or epidemiology. •Strong programming skills (R or Python preferred). •Knowledge
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at this Level Education/Training PhD in Electrical and Computer Engineering; Four (4) years training in wireless communication / remote sensing; knowledge of Zak-OTFS modulation preferred. Experience Experience
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Polarization Lab for scientific research on human-AI interaction on social media. The ideal candidate will possess advanced knowledge of R and/or Python software, have experience performing statistical analyses
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related dementias in Asia, and advancing our knowledge to better inform the design and targeting of person-centered community-based dementia care services. The scope of research involves the quantitative