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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
DFKI, with a specific focus on the data, alignment, and representation-learning foundations required for robust and generalizable sign-to-text translation. Motivation and context Sign languages
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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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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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learning, particularly flow-matching generative models and protein language models. The research will focus on designing efficient generative models able to produce realistic conformational ensembles while
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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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The successful candidate will join the Climate Physics team at ENS de Lyon (https://climatephysics-ensl.fr/ ), which currently consists of 5 permanent researchers, 7 PhD students and 2 postdoctoral researchers. We
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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are seeking a candidate holding a Master 2 degree in computational biophysics, structural bioinformatics, or a related field. Knowledge of statistical mechanics and/or machine learning would be an asset