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will work in a team of researchers, post-docs, and PhD students who are actively investigating various challenges and aspects of federated learning. The candidate should have knowledge in machine
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intestinal duct section. To achieve this, we will address the inverse design problem using physics-informed machine learning that consists of determining the optimal structure and material distribution
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(I3S), Sophia Antipolis Hosting lab: I3S & INRIA UniCA Apply by sending an email directly to the supervisor: emanuele.natale@univ-cotedazur.fr Primary discipline: Machine Learning Secondary discipline
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of the mines must also be considered. Recent advances in the geotechnical and geomechanical fields have led to a significant increase in the usage of machine learning (ML), thanks to its computational power and
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machine learning. We particularly value depth of knowledge, originality, and the potential for cross-disciplinary innovation. Relevant application areas may include (but are not limited to) natural
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. The candidate must be able to communicate in English (oral and written). The knowledge of the French language is not required. The candidate must have a strong interest in machine learning. Skills in
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properties changes. - The demonstration of the tear detection with machine learning classification applied directly on S-parameters of the MWI system without solving the inverse problem. The objective
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. The objective of this thesis project is to develop hybrid models that integrate electrochemical principles with machine learning techniques to analyze data from electrolyzers, predict performance, assess lifespan
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational