Sort by
Refine Your Search
-
implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
-
:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
-
College London, through the Imperial- CNRS International Research Lab on Multiscale Metabolism. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8199-HELDEG0-047/Default.aspx Requirements
-
. 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
-
the project or the position, please contact François Fripiat or Pierre-Henri Blard. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR7358-PIEBLA-006/Default.aspx Requirements Research
-
experimental validation of mathematical and computational models linking individual microscopic dynamics, information propagation, and collective structures (norms, social networks, global performance
-
-month postdoctoral contract focusing on data processing and the retrieval of information on aerosol heterogeneity from photometric observations made by the AERONET network. This position is part of a
-
has a strong societal impact ambition and requires particular care to protect intellectual property, while maintaining a collaborative research approach within a network involving other academic and
-
of these activities has led to support from several European, national, and regional projects, with current involvement in 1 Horizon Europe Doctoral Network, 2 regional projects, as well as the PEPR “Spintronics
-
of the project is to design, model and simulate neural networks based on magnetic skyrmion nucleation and propagation. The second objective is to fabricate these hardware neural networks, characterize