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numbers effects beyond what is currently possible. • Perform mathematical and numerical analysis of tensor network methods. • Work on GPU implementation of tensor network solvers. • Disseminate research
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research tasks, using both analytical and numerical methods, contributing to numerical validation of analytical predictions where relevant; (b) write up results for publication and present them at group
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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processing, analysis, and interpretation. 3) Developing innovative methodologies to improve the understanding of sediment dynamics. 4) Co-supervising the recruited study engineer within the project and
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Eligibility criteria The candidate must have a strong background in materials physics, atomistic simulation, or numerical modeling. Experience in molecular dynamics and handling force fields is required
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numerous academic partners. Working conditions: - Easy access to the Cronenbourg campus by public transport - The CNRS may cover part of the cost of travel tickets - Meals are available at a university
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activities will consist in: 1. developing steganalysis methods based on incompatibility search (i.e. study the fact that elements of the image are not compatible with natural images). 2. security analysis
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bacterial growth, survival and tolerance under various stress conditions, particularly antimicrobial stress. * Performance of microbiology, bacterial genetics and molecular biology techniques. * Analysis
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framework for the joint analysis of large-scale structure (LSS) and gamma-ray data. By combining these complementary probes of the same underlying matter distribution, the project aims to sharpen constraints
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design workflows for nanoporous carbon electrodes for sodium-ion batteries. The objective is to accelerate the exploration of virtual carbons with varied microstructures by combining numerical structure