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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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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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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 neuronal and vascular responses; Contribute to the instrumental optimization of the imaging system (detector configuration, illumination control, multi-camera synchronization); Analyze and interpret
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dislocation dynamics (DDD) code from TRIDIS, NUMODIS, or OpenDIS, based on the candidate's programming skills (Fortran, C++, or C). - Validate the DDD-FFT coupling required to optimize calculations
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collaboration with the University of Zurich. Mission The researcher will integrate, optimize, and stabilize the RNb-NeuS neural 3D reconstruction method within the AliceVision software architecture, enabling
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and optimization of metasurfaces exhibiting tailored spectral responses at selected near-infrared wavelengths • Development of angularly robust optical functionalities over wide ranges of incidence
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improved in several stages and continues to evolve. It will therefore be a question of carrying out the necessary technical upgrades and optimizing (fine-tuning) the different characterizations on reference
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in a strongly heterogeneous setting (e.g., CPUs+GPUs). Additionally, this work can benefit from the developments of WP1 and WP2 of Exa-SofT around the development of optimization techniques
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Louis Lions. - Design and implement innovative methods for the numerical solution of wave propagation problems within the FreeFEM software, using high-performance computing - Optimize the code