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transportation system which include compression and liquefaction. The impurities will pose a safety and lifetime estimation risk to the system as they are very corrosive. There is a need to expand the knowledge
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investigates a radically new paradigm for Embedded AI: enabling devices to dynamically compress and adapt neural networks directly on-device after deployment. Inspired by how humans continuously optimize
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the attachments exceed 30 MB in total, they must be compressed before uploading. Please note that information about you as an applicant may be disclosed publicly even if you have requested not to be included
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Scandinavian language or in English. If the total size of the attachments exceeds 100 MB, they must be compressed before upload. Please note that information on applicants may be published even if the applicant
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paid to performance across datasets, content sources, generation methods, and real-world transformations such as compression, resizing, and re-encoding. The final scientific scope will be refined
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/C++, and experience with deep learning frameworks such as PyTorch or TensorFlow Interest in hardware aware AI, including model compression, quantization, pruning, or efficient neural architectures
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materials • Performing laboratory thermal, moisture and strength tests following relevant European or National Standards (e.g. compression, thermal conductivity, water absorption) • Investigating means
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systems and edge intelligence. Advanced Architectures & Edge AI: Familiarity with modern neural networks is required. Experience with edge-specific model compression—such as knowledge distillation
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extraordinary multiscale geometric features and nonlinear mechanisms, enabling them to stretch, bend, twist, compress, or experience sudden changes in deformation without permanent damage. Most existing designs
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data-driven engineering sectors. Continuous Direct Compression (CDC) is rapidly emerging as a preferred method for pharmaceutical tablet production. A central challenge is powder blending, where active