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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
, designing natural language interfaces for more intuitive navigation of the BDC environment, creating BDC-specific foundation models, and enabling large language models (LLM) and machine learning-based
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About the Opportunity This job seeks a postdoc to work on one or more projects related to the development and assessment of wearable robotics. Possible projects topics include: Developing machine
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic
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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
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in machine learning, AI and programming skills, e.g. Python basic knowledge of materials science / materials engineering Leibniz-IWT is a certified family-friendly research institute and actively
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for the interview presentation. The interviews are performed by a panel of UCMR and UPSC researchers. The date of the final interview is 6-7 October 2025. Learn more about life as an ‘EC’ postdoc
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physics. Participation in detector research and development, especially for low-latency event selection in trigger systems. Development of new artificial-intelligence and machine-learning techniques
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics
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; identification of novel phases of matter through machine learning; and the development of new algorithms for the simulation of quantum matter. Applications from strong candidates with complementary interests
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performs MRI research and development of advanced multiparametric methods for the evaluation of primary and metastatic brain tumors. Recent work incorporates machine learning methods to advance