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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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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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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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: 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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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 25 days ago
. Candidates should upload a full curriculum vitae, a publication list, and a summary of current research interests with a statement of how they anticipate their research to mesh with the research goals
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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
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Observatory Network to model individual size distributions of macroinvertebrates and fish. This will include extensive travel to field sites throughout the US to conduct in situ metabolic scaling experiments
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Observatory Network to model individual size distributions of macroinvertebrates and fish. This will include extensive travel to field sites throughout the US to conduct in situ metabolic scaling experiments
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Observatory Network to model individual size distributions of macroinvertebrates and fish. This will include extensive travel to field sites throughout the US to conduct in situ metabolic scaling experiments
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) physics. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track