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the Manchester BHF CRE: Geometric Deep Learning for Complex Manifolds: Novel deep learning theories, models and architectures to simulate interactions within non-Euclidean, patient-specific cardiovascular
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University of California San Francisco | San Francisco, California | United States | about 1 month ago
. To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html
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knowledge of deep learning and computer networking/systems is required Experience with AI as a platform and expert use of AI as a tool strongly desired Experience implementing a language model is a plus Refer
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online at https://yourfuture.sdbor.edu . South Dakota Mines is committed to recruiting and retaining a talented workforce and offers an excellent comprehensive benefits package including medical, dental
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biology. Please include a cover letter with your application detailing your qualifications and experience for this position. Describe a deep learning project you have executed. Projects in computer vision
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reproducibility, Knowledge of deep-learning methods, At least one first author manuscript in the context of omics data analysis in an international peer-reviewed journal in good track for publication (to be
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.” As such, the Sanford School is committed to research that matters, teaching that empowers and inspires, deep engagement with the policy world, and to building a diverse and inclusive community
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data analysis. · Deep understanding of secondary education contexts, teacher practice, and adolescent development. · Knowledge of literacy education, social-emotional learning, culturally responsive
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machine learning, deep learning, or AI. Solid mathematical, algorithmic, or physics background, distinct analytical skills. Very good programming (Python, C++) and computer (Linux, Windows) skills
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microbiology. They learn contemporary microbiological, molecular genetic, bioinformatic, biochemical, analytical, and biophysical methods for studying microorganisms. Key research areas include physiological