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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 4 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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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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and BAT.jl projects. The position also offers opportunities to contribute to research in Bayesian inference and its application to physics in general. The DEMOS project aims to develop state-of-the-art
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provide a dynamic environment which empowers excellence with state-of-the-art technologies, cutting edge infrastructure, and a global scientific network. Contribute your knowledge, vision, and dedication
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: Cuantificación de Incertidumbre Bayesiana (Bayesian Uncertainty Quantification, BUQ) Appl Deadline: 2025/10/30 11:59PM * (posted 2025/09/08, listed until 2025/10/30) Position Description: Apply Position
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 23 days ago
Job Code: 07/2025 Job Offer from November 21, 2025 We are seeking to fill one postdoctoral position as part of the Simons Collaboration on Learning the Universe . In this international network
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models