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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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topology changes and transitions between different operating modes also constitutes a major challenge. Furthermore, this thesis will focus on the frugality of the proposed approaches, seeking to reduce the
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-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical
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cancer, inflammatory diseases, and complex infections. This PhD project aims to develop a new generation of multifunctional polymeric nanocarriers using Polymerisation-Induced Self-Assembly (PISA). Unlike
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interactions that control natural changes in Amazon climate and vegetation under different climatic forcings of the past two glacial cycles. This project provides a dynamic working environment between about
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modern machine-learning techniques, will be exploited to improve the discrimination between the different polarization states. The analysis will use the complete Run 2 and Run 3 datasets collected by
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not yet been detected and could be for the first time with the full dataset from the LHC's Run 3 (by combining different measurement channels). A relatively precise measurement of the self-coupling will
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the long term, situating them within their chronological, environmental, and climatic contexts across different geographical areas. You will be based at the Musée de l'Homme (Paris) within the ComE team
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assemble within large structures called sarcomeres, where they play a central role in muscle contraction. One of the functional regulation mechanisms of the sarcomere relies on the incorporation of different