678 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions at Harvard University
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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
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performance enhancement. The Postdoctoral Researcher will coordinate and lead a research project at the intersection of wearable sensing, controls, machine learning, AI, robotics, and movement science. They
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grade level 000. Corresponding salary information can be found in the job description above. Benefits Harvard offers a comprehensive benefits package that is designed to support a healthy work-life
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diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership
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diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership
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, United States of America [map ] Subject Areas: Computer Engineering / Cloud Computing , Cybersecurity , Software Biomedical Sciences / biochemistry , cancer , development , genetics , genomics , infectious disease , RNA
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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an individual with a PhD to conduct research in the area of biomedical informatics, multi-omic integratoin analytics and machine learning. In this role you will produce highly impactful biomedical informatics
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Services, Admissions, Faculty Affairs, and Student Affairs. Promote integration and interoperability across academic systems, learning platforms, and institutional data sources to improve reliability, reduce