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Field
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-informed neural networks (PINNs) and potentially generative adversarial networks (Pi-GANs). These models aim to predict cell fate and tumor development in CRC. The postdoc will collaborate with both
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will use finite volumes methods combined with physics-informed neural networks (PINNs) which offer a flexible technique that merges data-driven approaches with the underlying physics principles, enabling
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staff position within a Research Infrastructure? No Offer Description Title: “Synthetic Dataset Generation Technique to Optimize Neural Network Training for Seismic Data Prediction” Research Area
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computations emerge from cortex-wide neural dynamics across species. The PDRA will contribute primarily to developing and analysing Cortically-Embedded Recurrent Neural Networks (CERNNs) that simulate large
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research in network, cellular, and molecular neuroscience that helps us to understand the neuronal and glial basis of integrative brain function as well as their molecular underpinnings. INCC physicists
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research project “Is the brain network involved in sentence comprehension replicable and robust?”, led by Dr. Jurriaan Witteman. The project will investigate the neural mechanisms underlying sentence-level
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staff position within a Research Infrastructure? No Offer Description This research project aims to develop a synthetic dataset generation technique to optimize the training of neural networks (NNs
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diseases. The successful applicant will join a number of fascinating projects on engineering novel approaches to modulate physiology and neural activity in the brain, gastrointestinal (GI) tract, and other
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effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate
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postdoctoral fellowship at ENS Lyon in the field of machine learning. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE