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Public Health Academic Pipeline Program in Maternal and Child Health (MCH) of the Harvard T.H. Chan School of Public Health (HSPH) invites applications for a full-time postdoctoral training fellowship
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of Public Health is seeking a postdoctoral fellow in maternal health. The MCH Public Health Academic Pipeline Program in Maternal and Child Health (MCH) of the Harvard T.H. Chan School of Public Health (HSPH
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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and Applied Sciences Department/Area Electrical Engineering/Computer Engineering/Computer Science Position Description Project Deep learning plays an essential role in the operation of an autonomous
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Science, Computer Science, Applied Mathematics, Engineering and Physics. Additional Qualifications Expertise (or desire to work) in reduced order modeling, Causal inference and High Performance Computing
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, Biostatistics, Computer Science, Statistical Genetics, or a related quantitative field (by the time of appointment). Strong background in statistical or machine learning methodology, optimization, or high
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effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy
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effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy
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Science, Computer Science, Applied Mathematics, Engineering and Physics. Additional Qualifications Expertise (or desire to work) in reduced order modeling, Causal inference and High Performance Computing
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assessment, and ambulatory behavioral assessments to precisely track brain and cognitive change over short intervals. The program of research seeks to understand individual differences in aging trajectories