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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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theory, modeling, and AI-assisted optimization activities within the consortium. Reporting and dissemination of the results. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1074
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . The research associate will focus on Vision-Language Model based situation awareness and decision-making
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. Dickson. (2023) Chemical speciation models based upon the Pitzer activity coefficient equations, including the propagation of uncertainties. III. Seawater from the freezing point to 45?°C, including acid
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to accurately replicate the larva’s complete neuromuscular control system—a highly deformable biological system with a fully mapped neural connectome. The project leverages: Our current physics-based simulation
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 17 days ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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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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and collaboration across AI/ML, sensing, modeling, and control. A publication pathway spanning AI for science/instrumentation, process systems engineering, and control. Mentor programme as a new
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 18 hours ago
probability, fire intensity) under varying weather scenarios. The project will further explore how these simulation-derived metrics can enhance EO-based foundation models for wildfire risk assessment