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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Deep neural networks for astronomy and satellite imaging applications Where
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-aware AI under practical deployment constraints. Familiarity with efficient neural network architectures, including alternative attention mechanisms or mixture-of-experts models. Exposure to trustworthy
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; 2.) use invasive and non-invasive brain stimulation to probe the causal relationship between neural network dynamics and behaviour; 3) leverage these insights to pioneer closed-loop approaches
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; use invasive and non-invasive brain stimulation to probe the causal relationship between neural network dynamics and behaviour; leverage these insights to pioneer closed-loop approaches to therapeutic
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neural network has reduced this cost by a factor of 5000 (Radureau, 2025). This project aims to develop a mesoscopic radiative model to overcome the CFL constraint by adapting the average photon speed
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the foundational mathematics and programming skills necessary for creating basic neural networks and deep learning models from the ground up. Additionally, it is designed for those keen on comprehending
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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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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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motivated and intellectually curious PhD candidate to join the laboratory of Prof. Lorenzo Cingolani at the University of Trieste. Research vision: Can we selectively reprogram dysfunctional neural circuits