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- MOHAMMED VI POLYTECHNIC UNIVERSITY
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for tokamak fusion reactors. Specific responsibilities include: Neural Surrogate Modeling: Develop, train, and validate fast neural-network surrogate models (e.g., transport surrogates, edge surrogates, free
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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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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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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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include coastal features such as seagrass meadows, salt marshes, and dunes, and their dynamic interactions with waves, currents, and sediment transport processes. Artificial Neural Networks for coastal
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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by connectome-constrained artificial neural networks. Candidates from outside the field of neuroscience are encouraged to apply, but must be curious, persistent, and passionate to delve
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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification