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with GREYC (UMR CNRS 6072), the computer science laboratory of Université de Caen Normandie, particularly in machine learning and graph-based approaches. Depending on the scientific questions addressed
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of research design. Ability to conceptualize and construct tables and graphs. Basic knowledge of statistical and/or medical terminology. Basic ability to summarize statistical analyses for collaborator
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relevant to biomedical data: named entity recognition, natural language inference, or knowledge graph construction. Knowledge of graph data structures and graph platforms (Neo4j, Amazon Neptune
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deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations. Encode geological relationships in a knowledge graph that stores
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work, studying in disciplines ranging from atomic physics and graph theory to medieval literature and blind rehabilitation. Of 101 graduate offerings available, 30 lead to a doctoral degree. Connections
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text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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with demonstrated ability to implement and optimize AI/ML models for biomedical datasets. Preferred Knowledge, Skills and Abilities Mathematical Modeling: Strong foundation in numerical modeling, graph
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disinfect. Will chart for the dentist in an electronic record and obtain patient medical info, vitals, prescribed digital radio graphs and help with overall patient flow. Will place sealants, temporary
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minor graphing, and statistical methods. Ability to communicate efficiently and effectively with other lab personnel, students and investigators. Ability to analyze & interpret data. Preferred Education