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anticipate risks by integrating all human, technical, and organizational factors into a dynamic model of the OR. This project will be developed within the ICARE team (Artificial Intelligence, Computer Science
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Master's degree in Epidemiology, Public Health or a related field. Applicants should be enrolled in a Master program and the internship should be a mandatory part of the diploma; Experience in working
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master’s degree in mathematics, physics or informatics with a strong knowledge in machine learning. Skills: Coding in Python and/or R is required. Previous knowledge in archaeology and zoo-archaeology would
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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns
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to the ontogeny of health and disease. This project was selected for funding by the ERC-CoG-2024 call and the Impulscience program from fondation Bettencourt Schueller.
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, which performs numerical analytics during the simulation. This is necessary due to the ever-growing gap between file system bandwidth and compute capacities. To this end, we are developing the Deisa