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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials, machine learning, mobile health data, real world evidence, survival
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confidential basis until the completion of the search process. Inquiries, nominations, referrals, and CVs with cover letters should be sent via the Isaacson, Miller website: https://www.imsearch.com/open
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project is to develop scalable and privacy-preserving Bayesian computational algorithms. The position is intended for two to three years, with an initial one-year appointment renewable contingent upon
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at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary The faculty of the Department of Statistics at the University of South Carolina, Columbia
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analytical skills and a solid foundation in data synthesis, remote sensing, spatial analysis and Bayesian statistics o Strong proficiencies in R and/or Python o Experience with version control in GitHub
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied
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high-dimensional neural data. Approaches used include neural network-based approaches, Bayesian inference, and more Assisting with the oversight of day-to-day functions of the lab and shared lab spaces
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Posting Details Position Information Position Title Assistant Professor - 12 Month, Biostatistics Department Department of Biostatistics Posting Number F250011 Posting Link https