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: developing precise parton shower algorithms and studying their consequences in jet physics. - Develop a parton shower algorithm that is precise to the next-to-single log for ee collisions. - Extend
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) imaging instruments and quantitative calibration protocols. Optimization of μXRF data acquisition protocols to maximize signal complementarity. Participate in the development of algorithms to optimize HMSPL
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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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The postdoctoral researcher will develop a novel active remote sensing method for the detection and characterization of stratospheric aerosols by adapting the AEROTYPro/GRASP methodology to observations from
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Techniques Laboratory description : Laboratoire Interdisciplinaire Carnot de Bourgogne As part of the LabCom TeleMAQ (Testing the Limits of Quantum Machines and Algorithms) project, established between
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preservingmachine learning. Strong communication and writing skills; ability to work both independently and as part of a team. About the team The DATA team develops foundational mathematical and algorithmic
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interdisciplinary, and together we contribute to science and society. Successful candidates will join the Computational Biology group, led by Prof. Antonio del Sol, which develops computational models to address