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. The TEDIA project (https://exac-t.univ-tours.fr/tedia ) ai Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/136425 Requirements Specific Requirements Training in AI, ideally in
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robust descriptors (e.g., water activity, sorption, glass transition temperature, plasticization, porosity, internal distribution) and provide predictive guidelines to rationally select and design drying
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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is home to a consortium of postdoctoral fellows who provide modeling expertise for a wide range of projects as integral members of those research teams. Unit URL https://imci.uidaho.edu/ www.uidaho.edu
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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
clinical application. The focus is particularly on photon-counting computed tomography (PCCT), 4D MRI flow imaging, and AI-supported analysis and modeling methods (e.g., CT-FFR, predictive software models
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human interaction, learning, and emotional engagement through multisensory integration and scene understanding. Predictive and Adaptive Systems: leverage multimodal data to predict human intent, improve
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
their impact on the tumor immune microenvironment and immunotherapy response. 3) Developing clinical-grade mechanism-driven AI models (iGenSig-AI) for predicting responses to targeted therapies and
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scientific data, model architectures, and training dynamics influence scientific predictions. You will join a vibrant research environment at TU/e at the intersection of AI, scientific computing, and
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light–matter interaction through appropriate transport models, properly accounting for attenuation effects due to the materials. The activities will be carried out within the EIC PATHFINDER PREDICT
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 10 hours ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as