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X-ray diffraction techniques Participation in developing dynamic compression experiments at the HED Science Instrument at European XFEL, including shock diagnostics (SOP and VISAR) Preparation of PhD
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registered at the MIMME Doctoral School (https://mimme.ed.univ-poitiers.fr/ ). Institut Pprime is a dedicated research unit (UPR) of the CNRS. Its scientific activities span a broad spectrum ranging from
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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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PhD position in Adaptive Protocols for Decentralized Wireless Networks - Secure Mobile Systems Group
adaptive protocols and solutions. The research associate will contribute to building up the newly founded Secure Mobile Systems Group. Where to apply Website https://networks.imdea.org/job/phd-position-in
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 10 days ago
data compression in physics simulations (communication/computation time/cost, quality of the solution...). The benchmark will be realized with benchopt [3] and will benefit from the distributed coding
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encompasses model compression and optimization for edge deployment on UAV-mounted processors to support real-time inference. The candidate will collaborate with industrial partners for real-world data
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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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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
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects