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, storm activity, and other hazards using graph-based clustering, fuzzy machine learning, and reduced-order models – delivering scientific insight into where and when rerouting is needed. Real-Time Decision
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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learning, etc.); · Knowledge of explainable AI and Knowledge Graphs with ontology (e.g., RDFS, OWL); · Demonstrated experience with common advanced signal processing techniques (e.g. denoising
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Multidimensional Omics Data Analysis You will be responsible for Setup a knowledge graph in neo4J for microbiome research Integration
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of research being done. * Ability to set up and operate research instrumentation and equipment. Ability to make accurate assays and to prepare reports, charts, or graphs on results. * Ability to work
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 27 days ago
or term paper that you think best reflects your abilities A sample of code(s) which you have written, along with a summary of the outputs it generates (logs/graphs) in a single document We will be accepting
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spectral graph theory. The PhD will be supervised by Anurag Bishnoi.You will have the opportunity to collaborate with Postdocs, PhD candidates, and other faculty members of the research group. You will also
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., symmetries), learning on structured domains (e.g., graphs, manifolds) (to achieve data efficiency and respect constraints) Uncertainty Quantification: building models that quantify uncertainty associated with
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that integrates evolving knowledge graphs (KGs) with domain-specific foundation models to enhance diagnostic capabilities. This research will look into how knowledge graphs be designed and generated from
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affordance-level understanding of the environment over time. Emphasis will be placed on dynamically adapting the fusion process to modality confidence, constructing rich scene graphs that encode geometry