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Profile This multidisciplinary thesis requires strong expertise in several of the following areas: Robotics, computer vision, control systems, dynamic modeling, signal processing, or machine learning
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to align neuromorphic algorithms with the physical constraints of the target hardware. This hardware–software co design effort will involve: • Deepening and extending NSS-related machine learning and
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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/F) will work within the “RNA Architecture and Reactivity” unit and join the “Structure, Dynamics, and Targeting of Biomolecular Machines” team. This team currently consists of 6 researchers, 3
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of technological disruption driven by Artificial Intelligence, we propose to analyze the data and quantify these similarities by exploring various applications of machine learning methods. With the advancement of AI
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. Development and integration of state-of-the-art machine learning techniques in the analysis and event reconstruction will be a major component of this work. - Characterization of silicon detection modules using
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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the PhD project will be to acquire a thorough understanding of the PROVERIF resolution engine and its underlying theoretical foundations. The main scientific goal will then be to extend this resolution