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Field
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analysis, as many observed phenomena cannot be adequately modeled by stationary processes. The NOMOS project aims to develop a new generation of nonstationary models and algorithms for analyzing various
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for: • Contributing to various tasks related to the modeling of lipids and membrane proteins involved in lipid droplet biogenesis. • Developing and implementing the POP-MD algorithm in the OpenMM software
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, robustness under varying turbulence, and autonomy for distributed systems. To address this, the group integrates Artificial Intelligence into AO control loops, using deep learning to handle sensor
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- Design pilot and data collection of MEEG and behavioral experiments with Psychtoolbox, JsPsych, Pavlovia - Univarate and multivariete analysis (RSA, encoding and decoding models) of MEEG data at sensor and
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teams to contribute to the development of fundamental aspects of computer science (models, languages, methods, algorithms) and to develop synergy between the conceptual, technological and societal
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learning, and generative AI Design and implement algorithms for quantum-inspired and quantum-enhanced generative models Investigate theoretical foundations of tensor networks, entanglement, and collapse
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movements can be preceded by slow movements lasting from several days to several years. These movements can be detected and tracked by satellites, either using radar or optical sensors. Since 2016, data from
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optical medical devices. The works is aiming to develop optical devices and processing algorithms to guide neurosurgeons during the intraoperative neurofunctional assessment and connect these optical
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in the group optical medical devices. The works is aiming to develop optical devices and processing algorithms to guide surgeons during cancer surgery using fluorescence measurements. The post is based
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the necessary data from numerical models and observations to build the dataset; Identify the algorithms best suited to learn the targeted behaviors; Train the learning models; Validate their ability to predict