706 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" positions at Harvard University in United States
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Jane Coffin Childs Memorial Fund for Medical Research Eligibility: Applicants may be citizens of any country. Awards for non-U.S. citizens will be made only for study in the United States. American
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on projects at the intersection of computational neuroscience and machine learning. This position is part of a multi-investigator grant on the role of memory in intelligence systems. The Postdoctoral Fellow
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. Rather than being tied to a single lab, the RSE will provide shared, cross-project engineering support—helping multiple teams accelerate discovery by building and optimizing machine learning infrastructure
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trainings. Reports non-compliance incidents to the supervisor, manager, and/or Compliance Officer. Qualifications Basic Qualifications: The candidate is a graduate of a U.S. based residency program; board
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-grad or MS level with a desire to research and learn more about biomedical research, multi-omic integration analytics and machine learning. In this role you will produce highly impactful biomedical
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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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Burroughs Wellcome Fund: Postdoctoral Diversity Enrichment Program Eligibility: Limited to citizens of Canada and the U.S.; candidate must have no more than 48 months of postdoctoral research
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Damon Runyon Cancer Research Foundation: Fellowship Award Eligibility: Foreign nationals may apply to conduct their research only in the U.S. Level 1: Basic and physician-scientists must have
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, machine learning and AI, statistical computing, big data and AI applications and prediction in biology, medicine and infectious diseases. Potential research projects include (but are not limited
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic