222 high-performance-computing-postdoc Fellowship research jobs at Harvard University
Sort by
Refine Your Search
-
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
-
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
-
, 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
-
on conceptual development, data construction, analysis, and writing. Contribute to the design and implementation of quantitative text-analytic workflows and historical datasets. Produce high-quality scholarly
-
comparative genomics and sequence analysis. We expect the postdoc to be able to work independently and contribute intellectually to broader research themes and directions in the lab. The postdoc (or research
-
associate), with expertise in comparative genomics and sequence analysis. We expect the postdoc to be able to work independently and contribute intellectually to broader research themes and directions in
-
are on all meetings. Put research seminars into HKS HUB Events. Ensure ETSAGP-sponsored events are placed on the calendar Develop annual program of T&G Fellowship events and organize content of the events
-
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
-
, computer science, architecture, and engineering to develop scalable, data-informed solutions in sustainable design, construction, and energy management. The Cluster aims to modernize—and ultimately revolutionize
-
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