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like LiteBIRD and FOSSIL. We have a large international network of collaborators, and as a doctoral fellow you will have many opportunities to travel and collaborate with researchers at other
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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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University of Split, Faculty of civil engineering, architecture and geodesy | Croatia | 2 months ago
in karst using hierarchical Bayesian physical neural networks'' for a fixed period of time (maximum two years) for the duration of the project at the SARLU or Hydrotechnical Engineering. Where to apply
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to conduct cutting edge research in the field of uncertainty quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves getting Bayesian type uncertainty
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University of California San Francisco | San Francisco, California | United States | about 2 months ago
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 for clinical
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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of their mental models into a machine learning model, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation
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opportunities for collaboration with Michigan State University, the Milwaukee Police Department Academy, and IU’s network in cognitive modeling, AI, and human–AI decision research. This postdoctoral appointment
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significant external research funding. Experience supervising doctoral or postdoctoral researchers. Expertise in Bayesian and/or adaptive trial designs and dose-finding methodologies. Strong leadership and team
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induction, nearest neighbour classification, Bayesian learning, neural networks, association rules, and clustering are explored. The course also addresses approaches for handling unstructured data, including