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Field
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position We welcome applicants with a strong background in machine learning, causal inference, and statistics, who are eager to contribute to cutting-edge research at the intersection of these fields
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and acquired or developmental communication challenges to align with existing research in both departments. Desired areas of statistical expertise include Bayesian statistics, causal inference methods
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-reviewed literature - fluency in the R programming language - experience mentoring undergraduate students in research Preferred Qualifications: - experience with maximum likelihood or Bayesian inference
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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genealogical relationships and genetic divergence across species, but its complexity requires new methodologies for efficient analysis. This project aims to use Variational Inference (VI) methods, enhanced by AI
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for healthcare data research and willingness to adapt and learn new research methods Desirable criteria Competence in advanced analytical techniques including standard machine learning and/or causal inference
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of Physics and Technology, Mathematics and Statistics, and Computer Science. More about the position We welcome applicants with a strong background in machine learning, causal inference, and statistics, who
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Patrick Billingsley; Inference and Disputed Authorship: The Federalist, an application of Bayesian methods to fix the authorship of the Federalist Papers, by David L. Wallace and Frederick Mosteller
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. Applicants with backgrounds in statistical learning, network science, causal inference, Bayesian statistics, theoretical foundation of data science, mathematical statistics, and large-scale statistical
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related research directions in the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural