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2 Apr 2026 Job Information Organisation/Company CNRS Department Architecture et Réactivité de l'ARN Research Field Chemistry Physics » Biophysics Biological sciences » Biological engineering
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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
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how DNA LLM work, and develop solutions to integrate them into the neural network architectures developed by the lab. - Focus on developing new solutions for the scalability of neural networks and large
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. 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
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neuromorphic and energy-efficient hardware architectures. Early concepts – such as Intel's MESO logic gate – illustrate the transformative potential of multiferroic technologies and motivate deeper scientific
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compositions (carbon, metal alloys, oxides, clays, polymers). The laboratory develops and characterizes these materials, seeking to understand and control their architecture (structure, organization
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• Exploration of dielectric-based resonant and non-resonant nanophotonic architectures compatible with large-area fabrication • Demonstration of prototype metasurface devices combining optical selectivity
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systems. NumPEx aims to support the Computational Scientific and Engineering (CSE) community in leveraging the capabilities and potential of these new architectures through expanded and reusable exascale
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• Exploration of dielectric-based resonant and non-resonant nanophotonic architectures compatible with large-area fabrication • Demonstration of prototype metasurface devices combining optical selectivity
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extensive experience in the development and characterization of new renewable macromolecular architectures. The candidate (M/F) will carry out their work as part of a European project involving a dozen