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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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, or multitrophic interactions is a strong merit. Experience with computational methods such as multilayer networks, Bayesian inference, or higher-order network models is also a merit. The ability to communicate
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species, a storm-exposure hazard index, and bark-beetle susceptibility signals. Contribute to the development of an operational web-based monitoring service, including automated update workflows and a user
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enrich the knowledge base (i.e. learning by interaction); (iii) querying the knowledge base about what was useful in the past to predict actions that might be useful in the present, try them out and update
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species, a storm-exposure hazard index, and bark-beetle susceptibility signals. Contribute to the development of an operational web-based monitoring service, including automated update workflows and a user
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known under its acronym ESPRESSO (Epidemiology Strengthened by histoPathology Reports in Sweden). A description of the cohort can be found here: https://www.dovepress.com/cohort-update-espresso