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: proficiency in Monte Carlo simulation codes, such as GEANT 4 or PENELOPE. Bayesian statistics. • Laser polarisation system: appetite for experimental physics, experience with lasers not required but would be a
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 1 month ago
dynamics in health and pathology; (2) in silico models, including Bayesian models, neural mass models and spiking neural networks; (3) in vitro neuronal network measurements. Our aim is to innovate in
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applications. The project aims to address fundamental theoretical questions related to the representation and measurement of the polarization state, as well as the use of Bayesian and/or statistical learning
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appreciated. - Advanced statistical modeling (GLMMs, state-space models, stochastic Bayesian programming) in R - Experience with bioinformatics, if possible experience in the use of RAD-seq and/or lcWGS data
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foundations in classical probability theory and can be seen as a generalization of the Bayesian framework, bringing an additional degree of flexibility to express different types of uncertainty. In machine
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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
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courses within the research methodology (Data Collection) and statistics modules (Mixed Models, Bayesian Approaches, Network Models, etc.). Additionally, the candidate may also contribute to teaching across
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). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
: surrogates, neural operators, active learning, online training, Bayesian methods. Then -- start to work on possible generative methods for active learing (normalizing flows, diffusion models, generative
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore