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Field
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sciences, computer science, machine learning, and education research. Research Themes The research themes identified for the NTO postdoc include, but are not limited to, the following: Developing
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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Deep Learning libraries (e.g., Pytorch, Tensorflow, Keras) will be considered a significant advantage; Previous experience in image processing and\or computer vision will be considered an advantage
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applications and programs associated with the position (i.e. Microsoft Office suite) Strong attention to detail and follow-up skills Strong customer service skills and phone and e-mail etiquette USG Core Values
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employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more
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or computer vision); A strong scientific track record, documented by publications at first-tier conferences and journals (e.g., NeurIPS, ACL, ICLR, EMNLP, NAACL or COLM); Have excellent programming skills; Have
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improvements. Examples include optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques
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development opportunities, including a membership to Academic Impressions, LinkedIn Learning, and UT Dallas Bright Leaders Program. Visit https://hr.utdallas.edu/employees/benefits/ for more information
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 23 hours ago
, machine learning, and plant genomics. Our lab seeks to explore and understand the regulatory network of plant genes, their regulation in response to environmental stress at the single-cell level, and the
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models