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representations with probabilistic heads (GPs, Bayesian neural networks) for calibrated uncertainty estimates. Finally, the PhD candidate will focus on \textbf{active learning / adaptive design} for MF settings
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on soil carbon pools and fluxes, microbial communities, soil fauna, and ecosystem functioning; estimating carbon budgets of forest soil ecosystems using quantitative and probabilistic methods; developing
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 months ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional
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French National Research Institute for Agriculture, Food and Environment (INRAE) | Toulouse, Midi Pyrenees | France | 25 days ago
to reconstruct and date the evolutionary relationships of ancient and modern viral diversity. If applicable, you will employ phylogeographic and phylodynamic approaches to formally estimate past migration and
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expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry, etc. We work on a variety of learning problems, especially those involving supervised
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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analog electronic accelerators. You’ll collaborate closely with a multidisciplinary team of machine learning experts, software developers, computer scientists, fabrication specialists, and experimentalists
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. Want
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individuals and as communities comprising larger ecosystems. Traits are also often used as parameters in computer models of terrestrial ecosystems and even the entire Earth System (such as climate models used