30 finite-element-methods Postdoctoral positions at Conservatorio di Musica "Santa Cecilia"
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for Biomedical Informatics Research at Stanford University. This position emphasizes conducting real-world evidence studies using various causal inference methods (e.g., target trial emulation) to examine
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given to candidates studying early China using analytical methods such as zooarchaeology, paleobotany, ceramic analysis, and lithic analysis. The successful candidate will be expected to: Teach one course
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of the adaptive immune response to blood coagulation factor proteins deficient in individuals with the inherited bleeding disorder hemophilia, namely factor VIII and factor IX. The goal of the laboratory’s work is
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user
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radiolabeling of the resulting constructs. The fellow will conduct interdisciplinary research to develop unique translational therapeutics or methods to quantify the imaging data. Our federally-funded team is
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employ advanced analytical methods in large databases, which include claims data and electronic health record data in conventional structures and in common data models. Our research group prioritizes a
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the group are encouraged to develop new data analysis ideas and methods. The successful candidate will also be heavily involved in our ongoing project to develop and build new, high-speed data acquisition
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include a short section setting the context and motivating the work, a main section explaining the research questions and expected methods and data that will be used, and a short final section that
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immediately in the Department of Surgery at Stanford University. As part of the Asian Liver Center, our lab uses multidisciplinary approaches to identify and develop more efficacious methods for the diagnosis
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opportunity to contribute to methods development, particularly in refining circuit quantification and biotype stratification approaches to increase the clinical relevance and translatability of the research