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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-10248 Requirements Skills/Qualifications Expertise in computer graphics and AI, possibly including physical simulation and PDEs. Knowledge of C/C
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. Depending on the candidate's profile and interests, the thesis may develop along one or several of the following directions, at the crossroads of statistical physics, biophysics, and machine learning
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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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collaborations with biophysics laboratories. The project lies at the intersection of artificial intelligence, machine learning, computational physics, and molecular biology, and aims to contribute new
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) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical
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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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, 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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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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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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, or roundabout navigation will be considered. In this work, we also aim to explore the use of machine learning approaches [1][2] to personalize the driving system according to individual driver preferences