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Inria, the French national research institute for the digital sciences | Gif sur Yvette, le de France | France | 3 days ago
programming language and the PyTorch or TensorFlow environment is required. Experience in machine learning / neural networks is strongly recommended. Candidates must have validated a course in mathematical
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identification to characterize neural dynamics, closed-loop network behavior, and state transitions during sleep and seizure events. Real-Time Control & Optimization: Design, implement, and refine real-time
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of memristor-based circuits for neuromorphic computing: crossbar structures, vector-matrix multiplication (VMM), artificial neural networks, etc. Where to apply Website https://investigacion.ugr.es/recursos
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the start of the position Have practical experience in machine learning with Python, including training neural networks in PyTorch or a similar framework Have a solid background in signals and systems as
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neural networks, covering representation, optimisation, generalisation, robustness and reliability, while remaining sufficiently tractable to inform engineering practice. A key objective is to transform
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models ranging from baseline approaches to graph neural networks. You will also oversee the open release of project datasets, models, code and documentation. The successful candidate will join Oxford's
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Reference Number BAP-2026-500 Is the Job related to staff position within a Research Infrastructure? No Offer Description Modern embedded AI systems rely on Deep Neural Networks (DNNs) running on resource
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omics dataset generated to infer alterations in gene regulatory networks and cell-cell communication pathways responsible for the neurodegeneration. These predictions will be functionally validated using
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plus · Experience with one or more of representation learning, generative modeling, graph neural networks, transformers, or foundation model pretraining and fine-tuning is a plus
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specialized domains, such as Tabular Foundation Models. The core principle of these neural network models is that they are optimized for a specific form of data; in the case of tabular data, for instance