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the potential of sparse-to-sparse training of deep neural networks within reinforcement learning frameworks. This innovative approach holds promise for creating highly efficient and scalable AI systems capable
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efficiency of deep neural networks. Investigate the mathematical underpinnings of sparsity in deep learning and its effects on learning dynamics, and generalization. Implement and benchmark sparse training
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to staff position within a Research Infrastructure? No Offer Description The vacancy is focused on calibration in deep learning. Deep Neural Networks (DNNs) have demonstrated significant predictive
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evaluate products with a sustainability component. The challenge Reinforcement Learning (RL) has become a popular paradigm for automating decision-making under uncertainty in complex environments. Although
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techniques are preferred. Good knowledge/demonstrated awareness of sensor fusion and Artificial Intelligence/Machine Learning/Deep Learning concepts is an advantage. Ability to work in a laboratory environment
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in their work, facilitating the analysis of medical images and other data, for more rapid, comprehensive and accurate diagnosis, guidance of surgeries and more. Today, deep learning methods
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evaluate products with a sustainability component. The challenge Reinforcement Learning (RL) has become a popular paradigm for automating decision-making under uncertainty in complex environments. Although