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for the design, characterization, and validation of embolizing microparticles for endovascular applications. The project integrates advanced materials, biomimetic microfluidics, predictive modeling, artificial
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patient outcomes. More information about the lab can be found here: https://www.nathlab.org As a successful candidate, you will: Develop AI/ML models to predict patient outcomes, treatment response, disease
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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intelligence, as it develops model learning methods based on optimization, together with predictive validation procedures designed to assess the generalization ability of models on new data. Current approaches
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-quality agents, predictive models that can transfer across machine families, prescriptive decision support, operational dashboards, and ensuring the delivery of methodologies, documentation, training, and
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: multiscale and multiphysics modeling; constitutive model development; prediction of process-induced stress and warpage; design-for-reliability; and AI/ML methods linking metrology and test data to yield and
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based on the General NOAA Operational Modeling Environment (GNOME). Evaluate GNOME for application to the freshwater environment in the Great Lakes and identify gaps for predicting oil spill trajectories
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policies, with a focus on robot legibility, making learned robot behavior interpretable and predictable to the humans working alongside it. The Technical Associate will build and maintain the lab's VR-based
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 8 days ago
transformed into heterogeneous graphs, where rows become nodes and primary–foreign key relationships induce edges. GNNs can then propagate information across the relational structure and learn predictive
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Description The primary objective of this position is to bring about this paradigm shift by making quantitative predictions through coupled modeling using the modified GlaDS subglacial hydrology model and the