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of simulations and of reconstruction algorithms and related performance studies , characterization, installation and commissioning of the readout electronics for the first module of the far detector , data taking
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Searching for Triple Higgs Boson Production with the ATLAS Experiment at the CERN LHC School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr T Vickey, Dr C Anastopoulos
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research in machine learning (ML) for applications in High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time
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postdoctoral position within the CMS group, in the context of the “DRIFTS” project funded by the ANR. DRIFTS is a collaborative project between CEA/IRFU, CERN and LLR. It aims to demonstrate novel real-time
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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anticipated the candidate will have to spend some time at CERN during testing periods. Your key responsibilities will involve: Development of algorithm emulation software for trigger primitive finding
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at CERN via detector operation shifts and data analysis. The main goal of ProtoDUNE is to test the liquid argon detector technology and refine our understanding of how particles behave as they traverse
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post-doctoral contract to reinforce its activities on the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The successful candidate will work at L2IT. His or her work will focus
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the research collaborations Nikhef participates in are the ALICE, ATLAS and LHCb experiments at CERN, the KM3NeT neutrino telescope in the Mediterranean, the Virgo gravitational waves interferometer in Pisa
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and NWO-I Foundation, the Institutes Organization of the Dutch Research Council (NWO). Among the research collaborations Nikhef participates in are the ALICE, ATLAS and LHCb experiments at CERN