38 parallel-and-distributed-computing-"UNIS"-"Meta" Postdoctoral positions at Duke University in United States
Sort by
Refine Your Search
-
may include teaching responsibilities. The appointment is generally preparatory for a full-time academic or research career. The appointment is not part of a clinical training program unless research
-
research or scholarship. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless research training under
-
may include teaching responsibilities. The appointment is generally preparatory for a full-time academic or research career. The appointment is not part of a clinical training program, unless research
-
is generally preparatory for a full-time academic or research career. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is
-
Duke University, Electrical and Computer Engineering Position ID: Duke -Electrical and Computer Engineering -POSTDOCYIRANCHEN [#30336] Position Title: Position Type: Postdoctoral Position Location
-
, planning, coordinating, and executing scholarly events; and a deep interest in helping an existing program grow. There is considerable space for a visionary postdoctoral fellow to bring their interests and
-
or scholarship, and may include teaching responsibilities. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless
-
multiple affiliations. Postdoctoral FellowPosition Computational approaches to malaria parasite antigen diversity Duke Global Health Institute Be You. The Malaria Collaboratory is recruiting an exceptional
-
experience in optics, computation and electronics. Responsibilities will include designing and implementing optical systems based on quantitative phase imaging with digital holography and electronic systems
-
simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments