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Field
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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 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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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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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
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Reporting to Profs Annina Schmid and David Bennett. The post holder is a member of the Neural Injury Group and the Neuromusculoskeletal Health and Science Lab with responsibility for contributing