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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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deeper understanding is needed of how these microtissues remodel and fuse their matrices as they grow, and how this is governed by their mechanical microenvironment. As a PhD candidate, you will develop
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Newtonian dynamics) and linear algebra (vectors and matrices). Experience working with numerical integration techniques and/or rigid body dynamics. Understanding of core networking principles (e.g. client
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of material properties in short-fibre reinforced thermoplastics with different polymer matrices, such as polyamides, polyphenylene sulphide, and isotactic polypropylene. The study will focus on ageing induced
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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