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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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Job Description Do you want to figure out why Bayesian deep learning doesn’t work? And afterwards fix it? At DTU Compute we are working towards building highly scalable Bayesian approximations
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University of Split, Faculty of civil engineering, architecture and geodesy | Croatia | about 3 hours 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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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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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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opportunities for collaboration with Michigan State University, and IU’s network in cognitive modeling, AI, and human–AI decision research. This postdoctoral appointment is full-time and on-campus. Job Duties 80
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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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reinforcement learning, Bayesian neural networks, and Theory of Mind reasoning. They will engage in collaborative system design with DSTL and defence stakeholders to ensure that research outputs are both
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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
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Investigate the use of causal discovery methods in "concept drift" situations in structural causal models. In semiparametric Bayesian networks, investigate the selection of covariance matrices and the