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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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the creation of high-precision digital twins. Activity 1: Integration of Photometric Stereo in Meshroom - Implement processing nodes for normal field and intrinsic color estimation. - Integrate deep learning
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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and optimize their properties for neuromorphic computing through combined electrical and MOKE measurements, and train them to achieve artificial intelligence tasks. - Micromagnetic simulations - machine
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expertise or interdisciplinary experience is a major asset. Scientific skills - In-depth knowledge of teaching strategies, learning models, and educational technology. - Proficiency in the psychology of well
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collaboration between the Exa-SofT and the Exa-DI projects and better support multi-linear algebra and tensor contractions in exascale CSE applications and Machine Learning. As part of the collaborative process
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computer scientist with experience in bioinformatics, solid programming skills and knowledge in 3D protein structures. Machine learning skills and knowledge of Web development are a plus. Good interpersonal
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researchers with ample experience in MEG/EEG data analysis, BCIs, signal processing, deep learning for brain imaging analysis, biomedical statistics, dynamical systems and research on motor control. The lab has
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify