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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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. Demonstrated high level of achievement in related research productivity and academic writing. Technical skills in computer programming, algorithm development and deep learning model implementation, and practical
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 24 hours ago
mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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learning and network biology. • Background in cancer research. Pay Range: $62,232.00 - $81,000.00 To apply visit https://academicjobsonline.org/ajo/jobs/31509 and submit a cover letter, full CV, and contact
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salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL | Opportunities for Veterans
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and Intelligibility" or "Developing and Testing AI Models of Human Cognitive Function." Candidates can read more about these core projects at https://nam.ai.princeton.edu/research. The AI Postdoctoral
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Polytechnique de Paris. The group conducts research at the intersection of statistical learning, machine learning, and data science, with a strong focus on structured data, representation learning, and
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routine and complex machine learning and computational cancer analyses throughout the training period. Will be expected to develop, adapt, and implement new research computational approaches and tools
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assimilation, and at least a practical understanding of machine learning. Both profiles should bring a curiosity for bridging disciplines and a drive to innovate at the intersection of AI and ocean science
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data