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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Title Post-Doc Research Associate Vacancy ID PDS004492 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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Postdoc in Machine Learning for Oligopeptide Design (1.0 FTE) (V24.0584) « Back to the overview Job description The advent of modern machine learning (ML) methodology is accelerating scientific
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the following: Ensure you have all necessary documents available when starting the application process. You can review the additional job description section on postings for documents that may be required. Prior
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to structural biology, protein engineering, machine learning, molecular cloning, in vivo experiments, and/or CRISPR technology. Candidates must exhibit a strong command of written and spoken English, and
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Previous Job Job Title Post-Doctoral Associate Next Job Apply for Job Job ID 368970 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular Job Code 9546 Employee
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Previous Job Job Title Post-Doctoral Associate Next Job Apply for Job Job ID 369049 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular Job Code 9546 Employee
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the following: Ensure you have all necessary documents available when starting the application process. You can review the additional job description section on postings for documents that may be required. Prior
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interdisciplinary as it supports the advancement of constraint programming, machine learning, and artificial intelligence domains. The Post-Doctoral Researcher will be expected to perform research and development in
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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on several NSF and NIH-funded projects focusing on metacognitive processes and self-regulated learning across learners, STEM, and biomedical learning tasks and domains, using serious games, simulations, and