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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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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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to guide where to ablate. We call this electroanatomical mapping, and it is central to modern-day VT ablation. However, compressing these signals into 1 or 2 descriptive features means we are using only a
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, experience and personal qualities. All documentation must be provided in a Scandinavian language or English. If the attachments exceed 30 MB in total, they must be compressed before uploading. Please note that
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and personal qualities. All documentation must be provided in a Scandinavian language or English. If the attachments exceed 30 MB in total, they must be compressed before uploading. Please note that
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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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documentation you would like us to consider. Attachments must be uploaded as separate files. If the attachments exceed 30 MB, they must be compressed prior to upload. It is the applicant's responsibility
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-language-action models, imitation and reinforcement learning, world models, multimodal perception, model compression and edge inference. A key aim is enabling robots to improve beyond initial demonstrations