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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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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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and fabricating innovative microfluidic devices; - Developing experimental methods to measure tissue permeability, elasticity, and compressibility; - Performing transport experiments under controlled
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-language-action models, imitation and reinforcement learning, world models, multimodal perception, model compression and edge inference. A key aim is enabling robots to improve beyond initial demonstrations
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. This research will explore novel approaches for enabling AI at the edge, focusing on one or all of the following aspects: i) hardware-aware scaling, model compression, and novel approaches for diverse low-power
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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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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
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to education, experience and personal qualities. All documentation must be provided in a Scandinavian language or English. If the attachments exceed 30 MB in total, they must be compressed before uploading
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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