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-Track) Department: Medicine | School Biomed Sci - Biomedical Informatics Division of Biostatistics and Population Health (BPH, https://medicine.osu.edu/departments/biomedical-informatics/divisions
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
– a public-private partnership conducting phase II trials of new regimens for the treatment of tuberculosis (https://www.unite4tb.org/). Application of Bayesian methods for evidence synthesis
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, implementation science, geospatial analysis, biostatistics and research design, AI analytics, agent-based modeling, Bayesian modeling, causal inference, and measure development. We are seeking exceptional mid
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), National Animal Disease Center, Virus and Prion Research Unit, located in Ames, Iowa. For an introduction to the Flu crew at the National Animal Disease Center, please see: https://youtu.be/kOJy8tFTuiI About
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· Demonstrable problem-solving skills · Ability to propose and apply novel (literature based) and innovative ideas for solving a problem Desirable · Knowledge of Bayesian uncertainty techniques
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conducting solid oxide cells (E) Skills & Abilities Practical experience of applying computational techniques to the modelling of microstructure in solid oxide cell technologies (e.g. FEM, Gaussian, Bayesian
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at: https://industriesofideas.ai/ . Term-limited: This is a term-limited position for two years, with the possibility of renewal contingent upon satisfactory performance, conduct, continued availability
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: https://go.unl.edu/aboutus As an EO employer, the University of Nebraska considers qualified applicants for employment without regard to race, color, ethnicity, national origin, sex, pregnancy, sexual
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Statistical Analysis Plan guidance (APT-SAP)’ project (https://sheffield.ac.uk/ctru/current-trials/apt-sap ). Provide high-quality statistical advice and support to multidisciplinary research projects within
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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference