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Cardioembolic Stroke Risk Stratification using AI Accelerated Patient-Specific Blood Flow Simulation
stratification. Then, AI-based surrogate models will be developed in order to obtain fast simulations for large-scale risk stratification. The CFD model of the LA and the AI-based surrogate models will be
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. Job Description: Two postdoctoral researcher positions are available in the laboratory of Dr. Sarah Kim (https://pharmacy.ufl.edu/profile/kim-sarah/) to apply modeling and simulation approaches
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/124 Research Project: SMART-CM, Simulation and Advanced Modelling of Thromboembolic Risk The successful candidate will work within the framework of the SMART-CM project, as part of a research training
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. Potential projects include: Developing AI/multimodal models (e.g., integrating single-cell omics, spatial transcriptomics, epigenomics, and proteomics) to simulate virtual neural stem cells or organoids
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Requisition Id 16264 Overview: We are seeking a Computational Fluid Dynamics (CFD) modeler to apply modeling tools to simulate the complex behavior of fluids at all scales to support isotope
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modeling with experimental validation and has two major objectives: Development of a physics-informed neural network (PINN) framework You will design and implement a simulation framework to model
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with modeling teams to translate experimental findings into effective field descriptions suitable for simulation. This research environment offers extensive facilities for thin film fabrication
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 14 days ago
of hybrid modeling frameworks for electromobility, combining physical models, graph-based representations, and data-driven approaches. It aims at integrating large-scale mobility data to improve
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, security) into climate modelling, and assessing the impact of such SAI scenarios on the climate system. These scenarios will be implemented in the simulations via the controller mentioned above. The thesis
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is aimed at prospective PhD students who wish to conduct research on the fascinating topic of “dust in the Earth system” and who wish to combine model development with the investigation of the role