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leading scientists across Austria in an interdisciplinary environment spanning explainable AI, causality, knowledge representation, and neural networks. Research (90%) research in probabilistic machine
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optimization. At the same time, AI models, especially deep neural networks, are becoming increasingly complex, with energy consumption and carbon footprint emerging as major concerns. For instance, training a
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. Indeed, the methods currently used rely on optical image databases of various avalanche observations. A deep neural network was trained on this data to enable automatic avalanche detection FIGURE 1 (a) [1
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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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work with testing of bioelectronic devices and concepts using in vitro and in vivo biological models. The research will feature deployment of biomedical microdevices for neural recording/stimulation and
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passe pas à l'échelle et induit des latences de signalisation incompatibles avec la dynamique satellite [5–6]. Alternatives décentralisées via réseaux de neurones de graphes (Graph Neural Networks, GNN)[8
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,” Machines, vol. 9, no. 10, p. 210, Sep. 2021. https://doi.org/10.3390/machines9100210 [3] L. Podina, M. Torabi Rad, and M. Kohandel, “Conformalized Physics-Informed Neural Networks,” arXiv preprint arXiv
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, including deep learning architectures, self-supervised and unsupervised approaches, physics-informed neural networks, transformer-based models, and/or quantum-inspired learning techniques, capable
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Symbolic AI, Neuro-Symbolic AI, Agentic AI, Neural Networks for code vulnerability detection (Senanayake et al. 2024), SBOM tools, prompt vulnerability detectors, and static/dynamic analysis tools could
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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant