Les travailleurs du cycle du combustible nucléaire sont chroniquement exposés à de multiples sources radiologiques. À ce jour, les risques de cancers associés à une prise en compte simultanée de ces expositions, souvent corrélées, sont peu étudiés et les incertitudes de mesure sur ces expositions peu considérées. L'objectif de ce travail est de pro...
Cette thèse s'inscrit dans les domaines de l’apprentissage statistique et de l'analyse d'incertitude dans le cadre d'un problème de thermodynamique chimique.On s'intéresse à deux problèmes liés à la reconstruction d'une fonction $f$ multidimensionnelle : en premier lieu, un problème de propagation de l'incertitude dans le cadre du modèle linéaire b...
Cette thèse s'inscrit dans les domaines de l'apprentissage statistique et de l'analyse d'incertitude dans le cadre d'un problème de thermodynamique chimique. On s'intéresse à deux problèmes liés à la reconstruction d'une fonction f multidimensionnelle : en premier lieu, un problème de propagation de l'incertitude dans le cadre du modèle linéaire ba...
Grain growth is a thermally activated phenomenon that generally occurs during annealing processes. During grain growth, some grains grow while others disappear. This coalescence is a function of the grain size and crystal orientation. Classical statistical descriptors of the polycrystalline structure such as morphological and crystallographic textu...
Optimization of high-dimensional non-convex models has always been a difficult and fascinating problem. Since our minds tend to apply notions that we experienced and naturally learned in low-dimension, our intuition is often led astray.Those problems appear naturally and become more and more relevant, in particular in an era where an increasingly l...
A Bayesian statistics framework was created in this thesis work for developing clinical basedmodels in a continuous learning approach in which new data can be added. The objectiveof the models is to forecast radiation therapy effects based on clinical evidence. Machinelearning concepts were used for solving the Bayesian framework. The models develo...
A Bayesian statistics framework was created in this thesis work for developing clinical basedmodels in a continuous learning approach in which new data can be added. The objectiveof the models is to forecast radiation therapy effects based on clinical evidence. Machinelearning concepts were used for solving the Bayesian framework. The models develo...
A Bayesian statistics framework was created in this thesis work for developing clinical basedmodels in a continuous learning approach in which new data can be added. The objectiveof the models is to forecast radiation therapy effects based on clinical evidence. Machinelearning concepts were used for solving the Bayesian framework. The models develo...
A Bayesian statistics framework was created in this thesis work for developing clinical basedmodels in a continuous learning approach in which new data can be added. The objectiveof the models is to forecast radiation therapy effects based on clinical evidence. Machinelearning concepts were used for solving the Bayesian framework. The models develo...
A Bayesian statistics framework was created in this thesis work for developing clinical basedmodels in a continuous learning approach in which new data can be added. The objectiveof the models is to forecast radiation therapy effects based on clinical evidence. Machinelearning concepts were used for solving the Bayesian framework. The models develo...
