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theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
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communication and collaboration skills Preferred: Experience with simulation-based inference and Bayesian methods Familiarity with cosmological simulations or observational cosmology ML architecture design and
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Professor of Statistics Tenure-Track or Tenured Faculty Opening in the Department of Statistics at Rice University https://statistics.rice.edu https://engineering.rice.edu/ https://www.rice.edu/ Position
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, certification, and/or registration. Additional Qualifications Working knowledge and experience with R, Stan, Nimble, or other relevant analytical software. Knowledgeable of Bayesian statistical methods, numerical
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 3 months ago
-parametric Surrogates with Active Learning, SC AI4S 2024: https://hal.science/hal-04712480v1 Training Deep Surrogate Models with Large Scale Online Learning, ICML 2023: https://hal.science/hal-04102400v1 Loss
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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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for a Postdoctoral Scholar. The Scholar will conduct research on Bayesian spatiotemporal modeling methodology under the direction of Professor David Dunson at Duke on developing novel models motivated by
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designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work
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randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis. · Develop as an educator by taking an active teaching role in POCUS and EBM
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more