63 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" Fellowship research jobs at Nature Careers
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, or comparable research experience, along with significant experience in machine learning, computer programming, computational biological applications. A strong background in statistics and biology. Experience
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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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toto Dr. Divij Verma at Divij.verma@einsteinmed.edu For more information about our work, visit https://divijvermalab.com The Einstein base minimum salary for postdoctoral positions is $65,000. For a
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and expanding team. You’ll play a key role in our success through your code, publications, and strategic promotion of our work. * PhD in Computer Science, Biomedical Informatics, Machine Learning
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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