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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This research focuses on developing and validating Big Data-driven machine
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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
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, 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
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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
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Research Infrastructure? No Offer Description The requested figure will be responsible for developing and implementing both machine-learning methods for analysing images and audio files in Python, as
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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
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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
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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
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge
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fluorescence microscopy (SMLM, SIM), integrating physical-mathematical models, machine learning, and compressed sensing for accurate and efficient reconstructions. Applicants must submit a project implementing