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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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Postdoctoral Fellow to join the Tang Lab. The Tang Lab (https://tangxinlab.org/ ) works at the intersection of AI-driven automation, self-driving laboratories, and scientific discovery, in close collaboration
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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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distribution p(A) is typically incorporated in a Bayesian framework (e.g. enforcing that neighboring pixels are highly correlated). An additional difficulty here is that A is a structured geometric object: an
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University of Split, Faculty of civil engineering, architecture and geodesy | Croatia | about 5 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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. 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
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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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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