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Field
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respect to an infinitesimal perturbation of the dataset, provide a rigorous framework to: - **Identify the most informative samples** among the predictions of a deep neural network (DNN), with the goal
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural
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-film solar cells.” You will become part of an enthusiastic team working closely with collaborators at DTU Physics and DTU Nanolab to advance neural network-based methods for materials discovery. Project
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applied to control problems or tiny RL scenarios. Explore digital hardware realizations of the proposed RL algorithms within existing spiking neural network chip designs. Quantitative comparisons with
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. Of particular interests are (directed) hypergraph neural networks, which can be used to predict vertex (molecule), hyperedge (reaction), or hypergraph (network) features. While this project will be heavily
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./week) TV-L E 13for the DFG Project " The neural dynamics of causal evidence accumulation in multisenso-ry perception" as of 1.1.2026 or as soon as possible, limited for 3 years. The project investigates
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 2 months ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially
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). The candidate should have hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks (experience with graph neural networks is highly valued). Their background
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to WAN and inter-domain networking, Excellent command of foundational and applied AI technology, from neural networks, distributed reinforcement learning to agentic AI and recent developments in