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Field
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Fellow (CF). Equipment Utilized Computer skills including Word Processing, Data Base, Spread Sheets, and Graphing software, and commercially made educational materials (i.e., standardizedtests, language
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
of proficiency in either R or Python in the areas of machine learning, deep learning, statistical analysis, computer vision, and/or graph analysis Experience with data engineering to create data
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that support reliable cloud and enterprise services. A primary focus of the position is integrating operational data from Microsoft cloud platforms, including Azure, Microsoft 365, and Microsoft Graph
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the safe deployment of AI systems in real-world environments. Current research activities focus on topics such as: Constrained Generative models for graph and tabular data. Security, robustness, and
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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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entities, events, relations, causal claims and mechanistic pathways from scientific literature. Build pipelines that link textual evidence to biomedical ontologies, knowledge graphs, causal models and
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. Demonstrate the ability to use or learn to use graphing and scientific calculators. Ability to keep computing skills current, particularly the most commonly used software on the network. Bachelor's degree
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equivalencies: https://hr.uky.edu/employment/working-uk/equivalencies Required Related Experience 3 yrs Required License/Registration/Certification None Physical Requirements Sitting for long periods of time with
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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
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-agent systems b. Mapping and Scene Representations - Dynamic Scene Graphs modelling uncertainty - Unified situational awareness for multi-robot systems in large and degraded environments Qualifications