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that integrate physical constraints (Physics-Informed Neural Networks), as well as for the implementation and optimization of the associated algorithms. The researcher will analyze and interpret experimental and
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foundations and principles of Machine Learning, Linear Algebra (vectorial and matricial operations, optimization), with a particular focus on Neural Networks (pytorch), 3) problem solving skills, 4) familiarity
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of electrochemical reactions carried out under strong, controlled magnetic fields. Part of the work will involve establishing an in-situ characterisation method, supplemented by research into optimal experimental
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of the project is to design, model and simulate neural networks based on magnetic skyrmion nucleation and propagation. The second objective is to fabricate these hardware neural networks, characterize
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validated at CPPM. In parallel, the candidate will improve data reconstruction algorithms by using artificial intelligence techniques (e.g. neural networks), to optimize the separation between signal and
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techniques; Structural and functional characterization of interfaces and junctions, in particular through nanoscale analyses; Development, adaptation, and optimization of experimental methodologies