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be co-supervised by a researcher from Sorbonne University (regarding the computational method development) and a researcher from the University of Melbourne (regarding the application of the new
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study will leverage existing numerical resources and mobilize advanced methods from software engineering to develop a robust and adaptable framework for manipulating and coupling different models
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optimization-based network partitioning point to scalable, communication-aware control designs; stochastic MPC and co-design studies demonstrate methods for handling uncertainty and jointly optimizing assets and
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hundreds of hours of exposure) in order to estimate systematic errors. - Develop open-source analysis pipelines for extracting diffuse emission from objects with very low surface brightness. Take into
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the environmental impact of cloud infrastructures, making this PhD topic highly relevant to national and global sustainability goals. Scientific Objectives This thesis aims to develop novel methods for deploying AI
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reconstruction methods in DUNE, using both simulations and charged particle beam data from protoDUNES - Compare the performance obtained with single-trace events in the protoDUNE and SBND detectors - Study the
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reconstruction - Estimation theory - computational methods and deep learning approaches. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7249-HERRIG-026/Default.aspx Work Location(s) Number
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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Discovery”, with a strong scientific and environmental ambition: developing lower-footprint AI methods for real inverse problems in nondestructive evaluation. The topic has already passed the first ENACT
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the partner teams and a theory/experiment dialog will allow to enrich the project. The person recruited will have the opportunity to use a large panel of theoretical chemistry and molecular modeling methods