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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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to develop algorithms to factor linear differential operators using p‑adic techniques. The central question of this proposal concerns the efficient symbolic resolution of linear differential equations
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help reduce uncertainties in climate forecasts and better assess the effects of aerosols on atmospheric systems. The postdoctoral researcher's mission will be to develop and improve the WRF-Chem model
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, optimal transport, set evolution and shape optimization, calculus of variations), and/or convex/non-convex optimization methods (first-order algorithms, nonlinear/non-Euclidean gradient descents
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remains largely unexplored and represents a fascinating frontier for molecular computing. The development of new methods will be necessary to enable the manipulation of data within complex molecular
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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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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be
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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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validation in collaboration with the team. • Development of neuromorphic learning models based on the equilibrium propagation algorithm • Design of local learning rules for magnetic skyrmion systems
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communications that are viable, efficient and compatible with physical and human reality. Our work is based on mathematical and computer theories for the development of models and algorithms, validated by hardware