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France 91120, France [map ] Subject Areas: Applied Mathematics - statistical learning, graph learning or large language models Appl Deadline: 2026/03/24 03:59 AM UnitedKingdomTime (posted 2026/02/03 05:00
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and predict food-effector
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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NIST only participates in the February and August reviews. There is a growing need for high-performance materials for various technological applications. To address this need, the NIST-JARVIS (https
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advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research
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application! We are looking for a postdoctoral researcher to work on the fundamentals of knowledge graphs and virtual data integration. Work assignments You will actively participate and lead work tasks in two
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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with multitarget estimation for direction-of-arrival (DOA) detection and tracking in radar theory [12]. Graphs are a powerful data structure to represent relational data and are widely used to describe
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perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate therapeutic target identification. GenePPS aims to overcome current limitations of perturbation modelling