Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
carbon, water and energy states. The successful applicant will specifically support carbon and water cycle science, applications and process model innovations using CARDAMOM-based Bayesian inference
-
, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
-
for Bayesian inference Documented experience with programming in either Python or R. Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system Fluent
-
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
-
. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework for Bayesian inference Documented experience with programming in either Python or R. Foreign completed
-
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
-
testing, propensity score methods, meta-analysis, Bayesian inference, and a wide range of regression models (linear, logistic, Poisson, negative binomial, lognormal, Cox, mixed-effects, GEE, penalized
-
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
-
in knowledge-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov
-
, kernel machines, decision trees and forests, neural networks, boosting and model aggregation, Bayesian inference and model selection, and variational inference. Practical and theoretical understanding