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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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of algorithms based on artificial intelligence, since, to fully exploit the potential of AI, algorithms must be fed with data. This research project will examine these issues by focusing on three main areas
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– Advanced algorithms for energy sovereignty Research budget available over 5 years : Total Package ANR : 200 000€ Co-financing : 153 000€ Quota: full-time Website for additional job details https://www.insa
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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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algorithms will subsequently be developed, trained and tested to identify markers of reduced brain resilience during ageing. The project adopts an interdisciplinary approach at the interface of neuroscience
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
documentation Integrate such implementations in the FLUTE platform test algorithms and run experiments Prepare further research and development starting from the FLUTE platform. Where to apply Website https
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16 Jul 2026 Job Information Organisation/Company CNRS Department Centre de Recherche en Informatique de Lens Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage
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11 Jul 2026 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique de l'Ecole Polytechnique Research Field Computer science Mathematics » Algorithms Researcher Profile First
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systems. Specifically, the research will propose an integrated framework that will couple two core components: 1) advanced algorithms to optimise participation of HPPs, or more generally of VPPs integrating
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consensus. The successful candidate will develop the algorithmic foundations of this framework—making the motion-generation stack modular and heterogeneous (combining model-based control with learning-based