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Field
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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with, learning methods for robotics would be a plus • Experience with robotic systems and/or aerial robots is a plus Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR5216-VIRFAU-064
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-based, and machine-learning approaches - Identifying markers of intelligent, intentional, and malicious manipulations of detection systems - Developing detection and classification methods to distinguish
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out a rigorous scientific study aimed at comparing the performance of deep learning models in detecting complex visual anomalies. Take charge of the entire study, define the evaluation criteria and
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LPSM (Laboratoire de probabilité et modèles aléatoires) in Paris. Main mission : The project lies at the interface between quantitative ecology and statistical learning. It brings together the expertise
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. - Explore the response to controlled forces. Positioning of Magali SUZANNE's team within the MCD unit: We are interested in the process of morphogenesis and seek to understand how organs acquire
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the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments). Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR7271-SERVIL-002/Default.aspx Requirements Research
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scientific articles, presenting research results on conferences, and communicating with collaborators The Institute of Electronic, Microelectronic and Nanotechnology (UMR CNRS 8520 – https://www.iemn.fr/en
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computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work