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of participating media. In parallel, neural networks will be designed to approximate the resulting radiation fields, enabling significant reductions in computation time without compromising accuracy. The developed
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-efficient lighting, high- efficiency data communication and data storage, environmental sensing, efficient manufacturing process and efficient transportation. We aim to contribute to these challenges by
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validated on challenging AI applications, such as the separation of different brain processes in functional magnetic resonance data in a federated learning setting [8], or cross-scene hyperspectral/remote
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Information Selection process Interested candidates are invited to submit the following materials: • A complete CV, including a list of publications • A motivation letter • Contact information for references