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learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You will gain
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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16 Mar 2026 Job Information Organisation/Company IMT - Institut Mines-Télécom Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application
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at a computer for large portions of the day; repetitive motion; occasionally positioning patients over 25 lbs. Shift Monday – Friday, Day Shift; 7:30-6:00pm (40 hrs/wk), 4x 10-hour shifts Job Summary We
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experience aligned to the goals of at least one of the Centre for Data Science and AI’s with commensurate output. E2 Substantial experience in machine learning and AI, including experience in machine learning
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Computer Science, Data Science, and Artificial Intelligence, in both our West Lafayette and Indianapolis locations, as well as graduate MS and PhD programs. For more information, see https://www.cs.purdue.edu
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, and how their combination can improve safety signal detection. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical analyses
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systems for large-scale news and media data Multimedia information processing, retrieval, and verification Trustworthy, explainable, and robust intelligent systems Forensics, provenance, and authenticity