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Field
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learning algorithms constitutes a very promising research direction for energy-efficient and Edge AI applications: the goal here is to make these systems capable of learning per se, not just inference
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algorithms for exploratory network analysis. Our methodology focuses on robust heterogeneous graph analytics, designing temporal/dynamic data structures for motif extraction and data stream clustering
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6 Dec 2025 Job Information Organisation/Company CNRS Department CNRS Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage Researcher (R1) Country France
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or more of the following areas: + Quantum computing and quantum algorithms + Solid-state physics, computational materials science, or quantum chemistry + Battery materials modeling Excellent
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. The incumbent will have the unique opportunity to work at the intersection of cutting-edge science and real-world application, developing groundbreaking algorithms and systems that mimic natural processes
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep
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of the constraints on sequencing (read length, depth), and informatics (e.g., database composition, algorithm biases). Proposals should address these challenges with strategies to evaluate the metagenomic
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approach development for rescaling the temporal and spatial resolution of satellite images to be used in prediction algorithm and software development Develop Research Report, Conference Presentations
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algorithm development, numerical methods, or high-performance computing. Proficiency required in Python/C++ and scientific libraries (e.g., NumPy, SciPy). MATLAB is also desirable. Bonus: Familiarity with X
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research on AI/ML methods for accelerating integrated circuit (IC) design. * Participate in the deployment and testing of AI/ML-driven IC design algorithms to both open-source and industry-standard CAD tools