120 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" Fellowship positions in United States
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the foundation necessary to: identify the learning needs of diverse audiences create robust simulation cases and courses based on specific learning objectives develop and refine debriefing skills for all levels
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of immune cell function. These projects are focused on making safer and more effective cell therapies (e.g., CAR-T) and gene therapies for cancer and beyond. We are an interdisciplinary lab spanning
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of the Law Library Director and other librarians will learn about the Library's overall functions, policies, and practices in both the collections services and user services departments. In
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to evaluate pancreatic cancer pathology using human tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming
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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
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modeling, machine learning methods, and applications involving text and other non-traditional data sources. The fellow will contribute to and extend research in these areas, engaging in projects that develop
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opportunity to contribute to leading-edge research at the intersection of applied machine learning and clinical dental practice. As a member of our team, you will help translate contemporary data science
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and
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fluid dynamics. The successful candidate will be expected to work on all or a subset of the above topics, be proficient in working with large data-sets (observational or numerical), machine learning, and
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of faculty supervisor, develop novel techniques incorporating machine learning in particle physics event generators. Contribute to the development of machine learning driven techniques in the Pythia 8 event