107 phd-in-computer-vision-and-machine-learning Postdoctoral positions in United States
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- University of North Carolina at Chapel Hill
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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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University is seeking a Postdoctoral Research Assistant to support internally funded grant development and application activities connected to machine-learning-driven approaches to low-cost, low-carbon
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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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Division (MSTD), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). Examples on areas of research interest include but are not limited to: AI/machine learning algorithm development
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plans (Medical, Vision, Dental) Generous Retirement Plan – 10.12% annual compensation contribution, no match or vesting required Life and Disability Insurance Paid Time Off: 20 days of vacation per
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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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, Electrical and Computer Engineering, Computer Science, or a related field of study. Selected applicant must have PhD conferred upon hire. Preferred Qualifications Two years of prior work experience in small
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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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related field Knowledge, Skills, and Abilities: Familiarity with appropriate laboratory and technical equipment; ability to effectively use a computer and applicable software to create data bases, perform
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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