56 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" Fellowship research jobs at Nature Careers
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of Health (NIH) and the Department of Health and Human Services (DHHS). Within this program, the Section on Synapse Development Plasticity (Chief: Zheng Li, PhD, https://www.nimh.nih.gov/research/research
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(https://irp.drugabuse.gov/staff-members/da-ting-lin/ ) Note: This position is open to both U.S. and non-U.S. citizens. Selection for this position will be based solely on merit, with no discrimination
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Master's student, and several Undergrad students, and we plan to recruit an additional Postdoc and a graduate student over the next couple of years. More information about the Kim lab can be found here: http
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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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/research group and may suggest co-mentors across the University’s rich network. Final lists will be provided on the call website: https://careers.univie.ac.at/en/postdoc/e-steem . Your future tasks: Conduct
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department/research group and may suggest co-mentors across the University’s rich networkfinal lists will be provided on the call website: https://careers.univie.ac.at/en/postdoc/e-steem . Your future tasks
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: https://careers.univie.ac.at/en/postdoc/e-steem Your future tasks: You will: Conduct highly original and internationally competitive research in one of the designated fields. Develop and execute
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candidates will select a preferred PI and department/research group and may suggest co-mentors across the University’s rich networkfinal lists will be provided on the call website: https://careers.univie.ac.at
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candidates will select a preferred PI and department/research group and may suggest co-mentors across the University’s rich networkfinal lists will be provided on the call website: https://careers.univie.ac.at
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profiling, and other cutting-edge, high-dimensional tissue analysis approaches to evaluate pancreatic cancer pathology using human tissue specimens Assemble analysis pipelines using machine learning