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-driven methods provide excellent performance under low or cyclo-stationary regimes but struggle with highly dynamic and rapidly varying conditions; conversely, model-based state observers ensure robustness
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Débarre at the Lab. for Interdisciplinary Physics in Grenoble and of Dr Sigolène Lecuyer at the Physics Laboratory at ENS Lyon. You will be based principally in Grenoble, with secondments in Lyon and other
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Systems and Control division focusing on data-driven control methodologies. About the research project Model-based control is arguably the prime framework to perform certifiably-safe regulation of dynamical
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design strategies, while producing structured spatio-temporal datasets that will serve as input for realising predictive models. Objective 3 — Realize predictive tools for scenario-based assessment
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platforms. Experience in development of digital twins or physics-informed machine learning models. Experience in programming (e.g., Python or equivalent) and development of control or data acquisition
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water sensor at the molecular level. Our measurement techniques and numerical models based on constrained regularization algorithms allow us to link these measurements with other techniques including
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priority area of the University of Amsterdam is looking for a new PostDoc to amend our existing team (https://humane-ai.nl/ ). AI is everywhere. Chatbots, translation tools, shopping assistants, hyper
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models, including tissue harvesting and in vivo experimental work. CRISPR-based genetic perturbation approaches. Preferred Qualifications Education: Ph.D. - Doctor of Philosophy Certifications/Professional
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. Candidates should demonstrate technical proficiency in one or more of the following areas: Embodied AI and robot learning Vision-language-action (VLA) or multimodal AI modeling Perception, control
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of identity resolution concepts Familiarity with data quality frameworks and reconciliation patterns Knowledge of SCD Type 2 and historical data tracking Experience with Git-based version control and CI/CD