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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 28 days ago
retrieval: reconstructing the original digital data from the sequenced symbols. This PhD project focuses on the first and fourth challenges, by developing joint compression and error-correction algorithms
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[Srdinsek2025], the sole structure of the tensor networks that could be “salvaged” was the canonical form that provided the ability to dynamically compress/decompress. During this PhD, we will develop more; we
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. This research will explore novel approaches for enabling AI at the edge, focusing on one or all of the following aspects: i) hardware-aware scaling, model compression, and novel approaches for diverse low-power
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Description Compressed sensing 3D OCT Angiography We are seeking a highly motivated PhD candidate to join an interdisciplinary research project focused on next-generation Spatio-Temporal Optical Coherence
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and fabricating innovative microfluidic devices; - Developing experimental methods to measure tissue permeability, elasticity, and compressibility; - Performing transport experiments under controlled
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can be adapted to fit other ongoing projects in the research group for cooperation and mutual benefit. See https://www.inn.no/english/research/our-research/forest-research/ for examples of projects
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transportation system which include compression and liquefaction. The impurities will pose a safety and lifetime estimation risk to the system as they are very corrosive. There is a need to expand the knowledge
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supervisor), Professor Ingunn Studsrød (co-supervisor), and Associate Professor Marina Snipsøyr Sletten (co-supervisor). Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/302556/phd
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doctoral programme in Technology within three months of starting in the position. (https://www.usn.no/english/research/postgraduate-studies-phd/our-phd-programmes/technology/). The doctoral programme
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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