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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
-10346 Requirements Skills/Qualifications PhD in Computer Science, Machine Learning, Signal Processing, or a closely related field, completed or nearly completed at the start date. Strong background in
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Context Recent advances in computer vision and generative AI have enabled major breakthroughs in image and video understanding. However, modern deep learning models remain critically dependent
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
., Revenko, A., Teije, A. T., & Harmelen, F. V. (2023). Combining Machine Learning and Semantic Web: A Systematic Mapping Study. https://doi.org/10.1145/3586163 [2] Benoît Combemale, Pascale Vicat-Blanc
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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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will rely on: Developing machine learning-based surrogate AI models (physically informed neural networks) to predict the evolution of calcium carbonate precipitation rates associated with the reduction
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on machine learning, are monitored by an integrity controller. Autonomous navigation on open roads remains a challenge, due to the uncontrolled and uncertain nature of the environment, the multiple driving
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interactive systems (e.g., machine learning, trustworthy AI, AI-driven decision-making, human-centered AI, data-driven systems, intelligent perception, explainable AI). The recruited faculty member will
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multidisciplinary unit, with expertise in mathematical modelling and machine learning, wet-lab expertise in multiplex serological and genetic assays, expertise in diagnostic development and production, and expertise
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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
and broaden the scope of geophysical models. We will develop a neural solver that dynamically learns to solve the momentum conservation equations. Unlike traditional supervised learning approaches, our
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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