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for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. It comprises Jan-Willem van de Meent, who
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software engineering, Bayesian modeling and approaches to data analysis. Key Responsibilities: Preprocessing and data scientific approaches to analyzing human behavioral data Computational model development
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, 2022) and extended this to the triple equivalence between neural dynamics, Bayesian inference, and algorithmic computation (Commun Phys, 2025). -We validated it within in vitro neural networks (Nature
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modelling in Germany – preparing for contemporary and future risk” (VBD-MODE) consortium, is tasked with enhancing modeling and prediction of VBD emergence and outbreak patterns in Germany, and to estimate
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. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track record
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modelling in Germany – preparing for contemporary and future risk” (VBD-MODE) consortium, is tasked with enhancing modeling and prediction of VBD emergence and outbreak patterns in Germany, and to estimate
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there are innumerable examples of its application, one important observation is the low proportion of studies proposing the estimation of uncertainties (<5%). Yet uncertainties can be multiple and of different natures
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screens with robust guide demultiplexing and assignment, and cell-type annotation using bespoke references. Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
specifically, we use simulation-based inference (SBI) [1], a Bayesian approach that leverages deep generative models, such as conditional normalizing flows and score-diffusion models, to approximate