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in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? The successful candidate is expected
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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developed the SPARCED pipeline to convert structured lists of species, parameters, and reaction types into an SBML (Systems Biology Markup Language) model file, and we created a model based on one
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workflows: from full geometry-resolved models to reduced/identifiable representations for early-stage planning. Interface models with protection and control studies (WP2/WP3): generate Z/Y-based exports and
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adaptation options. Studies based on hydrological models developed by ICHARM are currently underway to address these needs. In addition, requests for the application of ICHARM’s hydrological models and related