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PhD position in Adaptive Protocols for Decentralized Wireless Networks - Secure Mobile Systems Group
://doi.org/10.1109/DCOSS52077.2021.00014 [E] D. Steinmetzer, D. Wegemer, M. Schulz, J. Widmer, and M. Hollick. Compressive Millimeter-Wave Sector Selection in Off-the-Shelf IEEE 802.11ad Devices, 13th
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month 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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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms
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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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provider how to adjust their compression depth, rate, and release to improve patient outcomes. The student will have the opportunity to work with a range of engineers, doctors, and paramedics and will help
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statistics This PhD project falls under the collaboration between Research Thrust RT2 Physics-based models, and Research Thrust RT3 on representation, compression, learning, and inference. For long-distance
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experience combined compression, tension, and shear while simultaneously undergoing severe thermal gradients. Yet most current material models and design approaches are based on simplified stress states and
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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