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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 8 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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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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mechanisms. The project will combine experimental studies with advanced signal processing and AI techniques (compressed sensing), aiming to detect early signs of failure and enable predictive maintenance
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implementations for compression, heterogeneous quantization, etc - reproduce state of the art network parameters with extreme quantization and compression of parameters - design and implement next-generation
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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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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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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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. Joerg Hoffmann at University of Saarland. 2) 1-2 PhD students working with Prof. Hendrik Blockeel and/or Prof. Jesse Davis on the topic of developing novel approaches for learning, compressing, and
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as