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Field
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate surrogates
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-like particles as candidates for dark matter. We perform tests of exotic models for Gravity beyond General Relativity, and cosmological measurements using GWs such as Hubble constant and probes
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-like particles as candidates for dark matter. We perform tests of exotic models for Gravity beyond General Relativity, and cosmological measurements using GWs such as Hubble constant and probes
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spaces of Hodge bundles and character varieties of surface group representations in complex and real reductive Lie groups G. While these spaces have been extensively investigated in the case of the complex
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. Skills in modelling and analysis using machine learning tools. Experience in environmental and economic assessment and in feasibility and replicability studies. Scientific production (Q1) and technical
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at CRAG (from basic science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https
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phenomenology in the Standard Model and beyond (including collider, flavour, Higgs and neutrino physics), QCD and strong interactions, lattice QCD, effective field theories in hadron and nuclear physics
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, modelling and simulation of refrigeration components and equipment. Participation in national and international refrigeration and heat pump projects. Publications in journals and communications at conferences
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techniques to create a model for the early prediction of victimization, offending and the victim-offender overlap, which could be employed in school and residential contexts to identify youth in urgent need
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sustainability of biomaterial manufacturing through safe design methods, machine learning, and predictive life cycle assessment as well as developing machine learning and hybrid digital modeling methods, combining