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Field
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learning and one PhD student with a keen interest in the algorithmic side of hyperbolic deep learning. Tasks and responsibilities: Conduct high-impact research on hyperbolic deep learning for computer vision
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mathematics, while abstract mathematical concepts generate fresh insights into algorithms and discretization techniques – critical for numerical computations and simulations. This convergence signifies a
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The role will engage in cutting-edge translational research that develops computational models for assessing cardiac biomechanics and for predicting outcomes in cardiac diseases. This includes (1) a
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 15 days ago
) for the 1D wave equation Testing of various gradient descent algorithms in different geometries Writing of an end-of-internship report Additional activities: Participation in the weekly ANEDP seminar
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insights into algorithms and discretization techniques – critical for numerical computations and simulations. This convergence signifies a pivotal phase in the mathematical sciences, where the divide between
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Theory, Statistical Inference, and Information Geometry. (2) Computing for AI: Programming Language Theory, Algorithm Theory, Combinatorics, Optimization Theory, and Homomorphic Encryption. (3) AI
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, materials science, or related fields. You must have proven expertise in at least one of the following fields: computational geometry, algorithm development, machine learning for image recognition
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computational counting and sampling, including developing new algorithms and analysis techniques. Typical tools and topics are Markov chains Monte Carlo, Lovasz local lemma, high dimensional expanders, geometry
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Qualifications/knowledge : PhD in computer science, with a specialisation in computer vision, digital geometry processing and/or machine learning. No specific knowledge about plants is required. Operational skills
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geometry altogether and operate in hyperbolic space. Our lab has published multiple papers showing that hyperbolic deep learning has strong potential for computer vision, from hyperbolic image segmentation