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Field
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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, leadership, and data science. Special training for writing successful ERC Starting Grants as a ‘ticket’ to an outstanding academic career. Being part of a thriving academic and social community in Vienna, one
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machines that both learn from humans and help humans learn. The postdoctoral fellow will lead a project using AI technologies to support active learning in young children, by empowering them to create
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machine learning are desirable, applicants from other quantitative fields (e.g. math, physics, statistics, computer science) who are eager to learn about neuroscience are highly encouraged to apply as well
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software skills in C++, python. Understanding of computing software development in the HEP environment, familiarity with machine learning (ML) techniques and experience with using ML software packages (e.g
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for this position will be key to coordinate and partake in collecting personal social network data in collaboration with a PhD candidate supported by the project. In addition, the successful applicant will be in
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record (EHR) as well as MyChart data, with the opportunity to work on applications of machine learning/deep learning/ Natural Language Processing in novel areas of healthcare. The position is open for a
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Charles Henry Turner Post-doctoral Fellow, Department of Geography & GIS, College of Arts & Sciences
techniques and geospatial tools Proficiency in Python/R, big data platforms, and GeoAI/machine learning frameworks Experience in managing large data sets Required Education Doctoral Degree in the appropriate