16 machine-learning "https:" "https:" "https:" "https:" "https:" positions at Politecnico di Milano in Italy
Sort by
Refine Your Search
-
machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
-
using machine learning methodologies; (2) the extension of an existing CFD framework for multiphase modeling to the case of PEC systems; (3) the implementation within the framework of a description of
-
, assess the health state of systems, and predict their future evolution and remaining useful life. The proposed approach integrates physics-based and data-driven modeling techniques, including machine
-
performance and remaining useful life prognostics, combining experimental data with physics-based and statistical models. A key role is played by Artificial Intelligence and Machine Learning techniques
-
materials according to the Lambert–Beer law, thus enabling an accurate description of PEC device behavior. In parallel, the coupling between kMC and CFD simulations will be achieved through machine learning
-
, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating
-
include: (1) implementing light–matter interaction in CFD via the radiation transport equation and suitable attenuation models; (2) integrating kMC-based surface kinetics through machine-learning surrogate
-
between Numerical Analysis and Machine Learning, with a focus on physics-informed machine learning. The goal is to design learning strategies that embed the structure of governing physical laws, enabling
-
decision-making across diverse applications in computer vision and data analysis. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional
-
computational resources to ensure the efficiency of analysis, modeling, and machine learning tasks. The researcher also contributes to defining policies that ensure security, service continuity, and scalability