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Principal Investigator of the OrDOTime project, and a collaborator on the project. Literature review, design of behavioural experiments, data collection, data analysis, writing scientific articles
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-Chem simulations for modeling aerosols and clouds in the Arctic region. - Analysis of the interactions between aerosols, clouds, and climate using data from the simulations. - Validation of the models
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of Euclid-DR1 data and, on the other hand, on an analysis of numerical simulations developed within the group. On the observational side, the focus will be on studying, in particular, the correlations between
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numbers effects beyond what is currently possible. • Perform mathematical and numerical analysis of tensor network methods. • Work on GPU implementation of tensor network solvers. • Disseminate research
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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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Eligibility criteria The candidate must have a strong background in materials physics, atomistic simulation, or numerical modeling. Experience in molecular dynamics and handling force fields is required
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). • Electronic transport measurements and charge sensing. • Numerical modelling and data analysis. The ideal candidate will demonstrate strong experimental skills, scientific autonomy, and a willingness to work at
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Metasurfaces: • Develop multilayered MSs using FDTD, RCWA, or inverse design methods to achieve broadband achromatic performance (400–700 nm). • Optimize MS geometries for high numerical aperture (NA), wide