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Field
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The postdoctoral researcher will join the "Network Dynamics & Computations" team led by Srdjan Ostojic and develop research projects on modeling neural circuits and their role in behavior. The work will focus
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is an interdisciplinary effort at the frontier between Biology (Genetics, Genomics), Bioinformatics, Artificial Intelligence (Neural Networks) and Statistics (LMMs). The aim is to join the
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. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE) enables computations to be performed directly on encrypted data
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or equivalent Skills/Qualifications - PhD in bioinformatics or related subjects - Expertise in python coding - Experience and good understanding of neural networks and machine learning - Fluent written and spoken
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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"Physiology of Neural Networks" team and will be in charge of a research project aimed at understanding the mechanisms of integration of cerebellar information in pyramidal cells and interneurons of the motor
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 1 month ago
the principals of open-science. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09545 Requirements Skills/Qualifications Strong background in recurrent neural networks (rate‑based
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 1 month ago
complexes. The successful candidate will develop novel graph neural network (GNN) architectures to learn dynamic information from molecular dynamics (MD) simulations of protein-protein and protein-nucleic
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in karst using hierarchical Bayesian physical neural networks'' for a fixed period of time (maximum two years) for the duration of the project at the SARLU or Hydrotechnical Engineering. Where to apply
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of multi‑radio systems, interference management, and energy‑efficient network design. Familiarity with machine learning applications in communications, including neural networks and federated learning