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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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groups, and a laboratory for research and innovation in the application of advanced computational and data intensive research methods, working in partnership with academics from all fields. We are a home
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parallel doctoral theses, we seek to create the CMD method, to make it publicly available by implementation in the opensource LAMMPS code for high-performance computing, and to apply CMD to explore
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learning Demonstrated expertise in software and algorithm development, computational methods, data analysis, modeling, machine learning, high-performance and parallel computing, or scientific simulation
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computational methods with a particular focus on deep learning and image analysis. The research is done in close collaboration with the BioImageInformatics Unit of SciLifeLab . SciLifeLab is a national resource
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documented experience in at least one and preferably more of the following areas: computational solid- and/or biomechanics; finite strain hype elasticity; finite element methods; numerical optimization methods
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algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational
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activities will be hosted by the Parallel Computing and Optimisation Group (PCOG) at SnT, headed by Dr. Grégoire Danoy. PCOG specializes in optimization techniques, with a focus on distributed computing and AI
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the University of Luxembourg in international research forums, fostering collaboration and visibility Participate in teaching activities The research activities will be hosted by the Parallel Computing and
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 3 months ago
13 Mar 2025 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile Recognised Researcher (R2