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Aix Marseille University, Interdisciplinary Center of Nanoscience of Marseille (CINaM) | Marseille, Provence Alpes Cote d Azur | France | 18 days ago
Is the job funded through the EU Research Framework Programme? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD fellowship
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. To make deep learning algorithms efficient in real life it is important to combine software and hardware optimization when creating new deep learning algorithms. During the PhD we plan to propose novel
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Title: Deep generative models for weakly-supervised improvement of photogrammetric modelsContact: Maxime Lhuillier (chargé de recherches CNRS, HDR) [email protected]: deep generative
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within a Research Infrastructure? No Offer Description Contexte et atouts du poste The main goal of this PhD project is to develope a deep learning framework for the design of cyclic peptides. This project
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We open 1 PhD/Postdoc position at CentraleSupélec /IETR in Rennes on Side-Channel Resistant DNN Hardware
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synthesizing new generations of PHC pi-conjugated organic matrices for applications in the field of PhOLEDs emitting deep blue and white light, which are currently the weakest links in this technology. Thus
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strengths of the position The main goal of this PhD project is to develope a deep learning framework for the design of cyclic peptides. This project has an important impact on designing theraputics to fight
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/ MSCA COFUND Marie Curie Grant Agreement Number 101126644 Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD position is offered under academic
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interactions at the crossroads of AI, applied math, physics and ocean science. It will provide a unique opportunity to the PhD candidate to gain a highly-skilled profile with cutting-edge expertise in deep
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scientific domains: numerical modeling, air quality, and deep learning. Its central objective is to enable precise assessment of population exposure to air pollutants, leveraging recent advances in artificial