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Field
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, thermodynamic aspects of the evolution of materials and interfaces, and resulting microstructures. - Related methods: Design of Experiments and Applied Statistics, with an openness to machine learning methods
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focus on the dynamic nature of phase transitions in APIs, using machine learning interatomic potentials (MLIPs) to construct force fields whose mathematical complexity will be carefully controlled in
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with machine learning approaches Knowledge of muscle mechanics (Hill muscle model or similar) Previous work on simulated bodies or animal locomotion Your Role You will work collaboratively with a
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. Skills in computational modelling or machine learning applied to brain signals are an asset. We are looking for a highly motivated, rigorous and curious researcher who is ready to invest themselves in a
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democratization of approaches using artificial intelligence based on Machine Learning (statistical AI), data lakes have also been proposed [4,5]. Objectives: Monitoring farming and agronomical activities is based
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anomaly detection using advanced and optimized methods. • Literature review (image processing, deep learning, vision-language models, diffusion models, etc.). • Generative AI for creating reliable models
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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to structured programming in C++ and Python - knowledge of linux / unix operating system - fluent knowledge of spoken and written English - fundamental knowlegde of machine learning (and statistics) - good level
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: Proficiency in English (knowledge of additional languages is a plus, particularly French) Additional Appreciated Skills or Interests: - Interest in managing long asynchronous tasks - Interest in machine
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 3 months ago
appreciated : Interest in privacy, data protection, or evaluation of machine learning models. Experience with reproducible experiments (code organization, documentation, version control) is appreciated