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Institut de Recherche en Génie Civil et Mécanique (GeM) | Saint Nazaire, Pays de la Loire | France | 5 days ago
are essential for: validation and enrichment of the predictive models developed in DECISION, definition of local constitutive laws and physically-based boundary conditions for modeling, improved understanding
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field
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estimator based on calibration embeddings, reducing calibration complexity. Validation will be conducted on multi-source PSOG datasets with controlled perturbations and diverse operating conditions. The final
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on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and
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learning, generalization/robustness and privacy aspects in scalable learning algorithms. Large‑scale optimization and control: Optimal control, model predictive control and other optimization‑based control
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. Analyze coupled failure mechanisms involving heat generation, gas evolution, internal shorting, and structural degradation, and connect them to cell- and pack-level safety performance. Design model-based
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. Key Responsibilities: Develop and implement perception and control algorithms for robotic arms and embodied AI systems. Assist in integrating multimodal AI models (vision, language, force sensors) with
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: Alexandre José Malheiro Bernardino (ist13761) Organic Unit: Scientific Area of Systems, Decision and Control Scholarship Theme: Computational Auditory System Simulators and Machine Learning-based Optimisation
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LES resolved wake dynamics and the coupling to aero-elastic models and wind turbine control. You will be a member of the Fluid and Offshore Mechanics section, which has a strong track record in offshore
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adaptability of robotic systems. In particular, the research will investigate distributed and/or game-theoretic control frameworks for multi-robot systems, integrating optimization-based planning with learning