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Are you passionate about compression, analytics, and machine learning for the Internet of Things and can you contribute to the development, operation and support in cutting-edge projects in strong
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-constrained platforms such as FPGAs, Raspberry Pi, or similar embedded hardware is essential. Familiarity with model compression, quantization, or hardware-aware AI optimization techniques is a strong advantage
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Stipend/Integrated Stipend in representation, compression
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PhD Stipend/Integrated Stipend in representation, compression, learning, and inference for classical and quantum data. At the Technical Faculty of IT and Design, Department of Computer Science, one
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on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms that protect the integrity and reliability of deployed AI
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challenge: how to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems (https://www.classique.aau.dk). CLASSIQUE will address a