Sort by
Refine Your Search
-
efficiency Optical neuromorphic computing is emerging as a promising alternative to classical electronic architectures, offering advantages in terms of speed, energy consumption, and parallelism. Nonlinear
-
18 Apr 2026 Job Information Organisation/Company CNRS Department Institut de recherche en informatique et systèmes aléatoires Research Field Computer science Mathematics » Algorithms Researcher
-
of deep learning in many disciplines, particularly computer vision and image processing. Consequently, coding architectures based on deep learning and end-to-end optimization have been proposed [Ding 2021
-
congested architectures. For more information, visit my professional website: https://iscr.univ-rennes.fr/daniel-muller Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6226-DANMUL-005
-
of Research Experience1 - 4 Research FieldChemistry » Computational chemistryYears of Research Experience1 - 4 Additional Information Eligibility criteria The candidate must hold a PhD in physics, chemistry
-
a major challenge, accounting for up to 50% of global electricity consumption by 2030. This situation is largely due to the Von Neumann computing architecture, which limits the energy efficiency
-
. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary results we have already obtained
-
offers and actions on https://cluster-ia-enact.ai/ . You will work in a rare environment at the intersection of frugal AI, analog computing, reconfigurable electronics and THz imaging. The PhD is directly
-
, engineers, PhD students, and postdoctoral fellows, at the interface between fundamental research, technological development, and experimental validation. Where to apply Website https://emploi.cnrs.fr/Offres
-
Processing Magazine, vol. 35, no. 1, pp. 126– 136, 2018. [8] J. Yu and T. Huang, “Autoslim: Towards one-shot architecture search for channel numbers,” 2019. [Online]. Available: https://arxiv.org/abs