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background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden Markov models, high-dimensional probability, stochastic modeling, and
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revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit priors on the latent variables. Having a clear
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selection criterion in some extent. This strongly suggests revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit
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the impact of social, environmental, and household factors on population health by integrating administrative data from various sources. What we are looking for Experience of Bayesian inference in a
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background in one of the following areas: Statistical Physics Applied Mathematics Statistics & Bayesian Inference Proficiency in Python is also expected. Contacts dbc-epi-recrutement at pasteur dot fr
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Master Thesis on Bayesian Optimization of Multi-stage Processes with Smart Inducing Point Allocation
and BoTorch is preferred Good coding practices Knowledge about inference methods, Gaussian processes, variational inference, Bayesian optimization Willingness to learn and research about SOTA topics
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. GPyTorch and BoTorch is preferred. Good coding practices Knowledge about inference methods, Gaussian processes, variational inference, Bayesian optimization Willingness to learn and research about SOTA
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, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding
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polycrystalline material during plastic deformation in order to eventually predict the manner in which materials deform and fail. As a first step, we wish to infer a distribution of the directions of deformation
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work in close partnership with the wet-lab team and use novel computational approaches and algorithms including A.I. and Bayesian statistical methods to infer causal relationships between mtDNA variants