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candidate will have a proven track record of applying rigorous epidemiologic and statistical approaches to complex datasets, collaborating with multidisciplinary teams, and advancing knowledge in pathogenesis
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from the TexNet earth observation program or assets that provide quality data. Compare different methods and tools for deformation modelling. Engage in outside funding activities and promote programs
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and manipulating complex data structures, Bayesian modeling, analyzing nested longitudinal data, and who are familiar with techniques for handling challenging data (e.g., highly non-normal distributions
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Davis Health Policies or Collective Bargaining Agreement • Continuing Education (CE) allowance and Education Reimbursement Program as stipulated in the UC Davis Health Policies or Collective Bargaining
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Bargaining Agreement • Continuing Education (CE) allowance and Education Reimbursement Program as stipulated in the UC Davis Health Policies or Collective Bargaining Agreement • Access to free professional
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at the Computational Imaging Research Lab (CIR) and the Department of Cardiology at the Medical University of Vienna and the Visualization and Data Analysis Research Group (VDA) at the University of Vienna. Your Tasks
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candidate with a strong background in computational fluid dynamics (CFD) and specialized expertise in hemodynamics associated with coronary artery disease (CAD). The ideal candidate will hold a PhD in
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genetics, and may include expertise in one or more of the following: computational biology, statistical genetics, population genetics, biomedical informatics, complex genetics, clinical genetics
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, including patch-clamp electrophysiology, high-resolution imaging, chemogenetic manipulation of glial activity, and behavioral assays in rodent models Analyze and interpret complex datasets from
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, Pennsylvania 19004, United States of America [map ] Subject Areas: Industrial Engineering Statistics Economics Management Science & Engineering Mathematics (more...) Physics Computer Science and Electrical