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to regulatory agencies, investors, pharma companies etc. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/human-organs-on-chips/ What you’ll do: Develop models
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of the Kempner Institute. A list of two to three proposed mentors from current faculty associated with the Kempner Institute or working on machine learning, artificial intelligence, or computational neurobiology
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, military service, pregnancy and pregnancy-related conditions, or other protected status. Please note: This position is contingent upon funding and satisfactory performance. Due to the importance of in-person
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. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern machine learning methods as well as in biological data analysis are needed for the position
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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for purposes of collective bargaining and matters affecting your compensation and working conditions. Basic Qualifications PhD in Computational Biology, Bioinformatics, Machine Learning, Data Science, or a
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, Military Affairs, Military Strategy, Intelligence, Nuclear Security, Nuclear Strategy, Arms Control, Nonproliferation, Terrorism, Counterterrorism, Cybersecurity, Emerging Technologies and Security
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novel findings that inform disease etiology. The candidate should be interested in focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research