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-career scientist to develop cutting-edge machine learning approaches for understanding and designing pathogen antigens. This is a unique opportunity to help shape a new research program at the intersection
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-disciplinary innovation Publications in top-tier computer conferences and journals related to AI and machine learning, including but not limited to NeurIPS, ICML, ICLR, JMLR, CVPR, ICCV, ECCV, IJCV, TPAMI, ACL
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Description of the offer : The proposed project aims to assess the spin-charge and orbit-charge conversion efficiency of various systems (oxide 2DEGs, Rashba interfaces, etc) using spin-pumping from
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We are looking for a co-design engineer for future computing architectures in HPC. This position, in a fixed-term contract, is based at the Nano-Innov site of Paris-Saclay, Essonne (91). As part of
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systems Computer science » Programming Computer science » Computer systems Engineering » Electronic engineering Computer science » Systems design Researcher Profile First Stage Researcher (R1) Recognised
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 1 day ago
recently created team involving researchers from Inria (Rennes, France), Ifremer (Brest) and IMT Atlantique (Brest). Inria is one of the leading research institute in Computer Sciences in France, and Odyssey
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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recruited for this position will be responsible for analysing disassembling work activities and related workplaces, within existing recycling channels and highlighting the current organizational work
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Inria, the French national research institute for the digital sciences | Grenoble, Rhone Alpes | France | 1 day ago
the Thoth project team (https://thoth.inrialpes.fr/ ) within the Inria Centre at Université Grenoble Alpes (https://www.inria.fr/en/inria-center-universite-grenoble-alpes ) to work under the supervision
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models capable of inferring the IPLS directly from widely available genomic and epigenomic data. Broadly, the project aims to develop integrative approaches at the interface of (epi)genomics, machine