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Knowledge of or experience with semantic modeling, knowledge representation and automated reasoning Knowledge of or experience with knowledge-based engineering and system engineering Knowledge
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form of topic modelling as a method for automated content analysis. Topic models are a probabilistic method that allows organizing the contents of a text corpus into a set of semantically coherent
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, systems, and constraints explicit and machine‑interpretable, and (3) is suitable for computational reasoning and AI-based methods. In doing so, the project creates the semantic foundation that enables AI
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, and institutional content). • Implementation of Retrieval-Augmented Generation (RAG) with semantic and geographic indexing. • Fine-tuning to improve cultural coherence, multilingual responsiveness, and
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to the user, which pictures to show? A third possible topic is performance improvement of using a graph-based analysis and/or infrastructure. Typical RAG systems use a semantic search based on embeddings. NEO
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- Multimodal Perception and Localization for Robots in Extreme Visibility Conditions - Source Localization and Hazard Assessment Using Multisensor Fusion. - Semantic-Based Exploration Strategies for High-Risk
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application development. Deep Learning techniques, Data Engineering, and Semantic Technologies Open-source artificial intelligence, machine learning, statistical estimation methods, software tools, and big-data
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project teams. Comprehensive IT user skills Desirable qualifications are Excellent knowledge in the fields of multimedia systems, digital media technologies, semantic analysis of multimedia content
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skull implantable stimulation hubs forming a communication network for 3D targeting by self-organization, providing semantic information transfer and synchronization, and thus enabling self-adjusting
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ticket discounts Access to UT Austin's libraries and museums Free rides on all UT Shuttle and Capital Metro buses with staff ID card For more details, please see: https://hr.utexas.edu/prospective/benefits