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research, all with the goal of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and six
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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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and is associated with the management of the Institute's own 500 m² clean room facilities (https://www.igm.uni-stuttgart.de/en/research/clean_room_lab/ ). We are looking for an individual with
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such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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Università degli Studi di Roma Tor Vergata - Dipartimento di Ingegneria dell'Impresa Mario Lucertini | Italy | 2 months ago
research relies on a real-world dataset covering the entire Italian Twitter sphere in 2022 and aims to evaluate modular Graph Neural Network architectures for scalable and interpretable node representation
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environmental perturbations become biologically embedded through the microbiome, we will define the signalling networks and neural circuits that shape brain and behavioural function. This programme seeks
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human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus
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at the intersection of AI, deep learning, computational neuroscience, and vision science. You'll develop biologically realistic neural networks to understand how individual differences in the brain shape perception
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staff position within a Research Infrastructure? No Offer Description Activities: The scholarship recipient will be responsible for implementing and comparing quaternion-valued deep neural networks