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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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Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In this project, we will focus on increasing validity
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radiocarbon dating and Bayesian modelling. The postdoctoral researcher will contribute to an ongoing research project, “Milestone”, headed by Associate Professor Sarah Croix. The appointment begins on 1 April
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of autonomous flow reactors for chemical synthesis. The project aims at 1/ developing a new optimization Bayesian algorithm and 2/ improving the process-control software already developed in the team
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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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contribute to the excellence of our academic community. We are looking for a postdoctoral researcher with expertise in Bayesian hierarchical spatio-temporal statistics and measurement error methods for a 3
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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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Max Planck Institute for Physics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
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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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with
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quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with computer vision tools (e.g., MediaPipe