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: Developing novel techniques to understand how information is processed within deep neural networks. Developing methods that achieve high accuracy while also being safe, interpretable, responsible, and reliable
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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to modulate physiology and neural activity in the brain, gastrointestinal (GI) tract, and other peripheral organs. These projects have a high potential for translation towards treating a variety of neurologic
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implementations (e.g., biophysical models), as well as models of machine intelligence (e.g., deep convolutional neural networks). We test the models' predictions in our empirical studies with human participants
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learning, physics-informed neural networks, graph neural networks, transformers, convolutional defiltering methods, etc.) for the integration in multi-physics simulation codes You will develop code for and
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or spike correlation patterns limited to local neural circuits or span across brain regions? Set up a network model to reproduce the main results and provide potential neuronal mechanisms. Existing
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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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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 2 months 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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Physics and AI: Physics-Informed Machine Learning (PIML): Physics-informed neural networks, with applications to Fluid dynamics, plasma physics, elasticity, weather modeling AI for Scientific Discovery in
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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